- published: 25 Jan 2016
- views: 2152
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Gary Marcus, CEO at Geometric Intelligence, presented at FirstMark's Data Driven NYC on January 19, 2016. Marcus' talk focused on ways A.I. is stuck.
Geometric Intelligence is redefining the boundaries of machine learning through innovative, patent-pending techniques that learn more efficiently from less data.
Data Driven NYC is a monthly event covering Big Data and data-driven products and startups, hosted by Matt Turck, partner at FirstMark.
FirstMark is an early stage venture capital firm based in New York City. Find out more about Data Driven NYC at http://datadrivennyc.com and FirstMark Capital at http://firstmarkcap.com.
フルHD動画です。
※ヘッドフォン推奨。
FM64音+PCM12音使って本気で作ってみました。
後半はオマケ曲です。
8音ずつのMDXファイルを8つ作って同期再生させる事でFM64音を実現しています。
外部エフェクターの類は一切使用せず、FM音源によるデチューンと音量ディレイのみです。
PCMはドラムとボイスにのみ使用しています。
普段はニコ動に投稿しています。
これまでに投稿した動画はこちら（ニコ動）→http://www.nicovideo.jp/mylist/31231343
この動画のメイキングはこちら（ニコ動）→http://www.nicovideo.jp/watch/sm19191157
- published: 23 Oct 2012
- views: 16814
Movidius CTO David Moloney talks about the future of pilot-less drones thanks to machine vision and artificial intelligence. Also discussed is a new approach to storing 3D volumetric data at a tenth of the current size, paving way to a future where flying robots can autonomously obtain and act upon city-sized amounts of geometric data.
Learn more about how machine vision is revolutionizing our devices at www.movidius.com
- published: 19 Nov 2015
- views: 573
The St. Ghislain, Belgium data center wall goes from a blank canvas to a colorful mural by Oli-B in this timelapse video. See more of Oli-B’s murals at g.co/datacentermurals
- published: 12 May 2016
- views: 2219
MIT 6.851 Advanced Data Structures, Spring 2012
View the complete course: http://ocw.mit.edu/6-851S12
Instructor: Erik Demaine
Point location and range searching: persistence, retroactivity, dynamization of augmentation through weight balance, and fractional cascading.
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
- published: 18 Dec 2013
- views: 7855
For tips on how to plan successful data mining projects, read this IBM white paper: http://bit.ly/1mDN62O
More info at https://www.analyticszone.com/blogs/48ab01ca-89ca-4827-890f-f202be87cd81/entry/analytics_education_series_an_introduction?lang=en_us
Aurélie C. Lozano will cover model selection, factor analysis, PCA., joint feature selection and estimation, Lasso and more. Key discussion points covered in this webcast will be:
Analytics Capacities Landscape
Why Dimension Reduction?
- Solution: Dimension Reduction
- Example: Document classification
Methods for Dimension Reduction and Applications
- Main classes of techniques for Dimension Reduction
- Feature Extraction/Reduction
- Unsupervised Feature Reduction
- PCA
- Geometric view of PCA: 2-D Gaussian Scatter plot
- Example of Application for PCA: Clustering
- Non-linear PCA
- Multidimensional Scaling (MDS)
- Manifold Learning
- Manifold Learning: ISOMAP
- Example of Application: Hand
- Supervised Feature Reduction
- Linear Discriminant Analysis (LDA)
- LDA: Sample applications
- Supervised Principal Components
- Supervised PCA: Example of Application
- Feature/variable selection
- Feature Selection
- Feature Selection: Filter Methods
- Feature Selection: Wrapper Methods
- Sample Algorithms
- Sample Applications
- Sparse Learning
- Why Sparse Learning?
- Sparse Learning: Joint dimension reduction and estimation
- The Lasso: The most popular sparse learning method
- The Group Lasso: Extends the lasso to accommodate grouped selection
- Various types of sparsity on matrices
- Various types of sparsity (matrix factorization)
- Application to GWAS - Genetic Basis of Complex Diseases
- Application to climate change attribution
- The approach: Sparse Learning with spatio-temporal data
- Application: Key influencers in online communities
- Application: IBM Lotus Bloggers
- Sparse Learning on Matrices: Image denoising
- Sparse Learning on matrices: network inference
- published: 23 Jan 2014
- views: 3056
It's all about calculating the area for a defined boundary. One advantage with GIS is the ability to define boundaries, thus allowing us to Calculate Geometry. In addition to basic calculations, the ArcMap program can also standardize area in metric and empirical units.
The Toronto Bikeway, Parks and Regional Boundaries were attained from the Toronto Open Data.
Video contains public sector Datasets made available under the City of Toronto's Open Data Licence v2.0.
http://www1.toronto.ca/wps/portal/open_data/open_data_fact_sheet_details?vgnextoid=59986aa8cc819210VgnVCM10000067d60f89RCRD
Visit my blog for more information: http://teachugis.wordpress.com/
- published: 22 Apr 2013
- views: 1662
Thanks for joining. Sorry I didn't download a stream dataset. This video will only focus on calculate geometry. 4 of 4.
TheRegional Boundary was attained from the Toronto Open Data and the DEM a 1:50,000 attained from GeoBase.
Video contains public sector Datasets made available under the City of Toronto's Open Data Licence v2.0 and GeoBase® (http://www.geobase.ca/).
Visit twitter feed for more information:
https://twitter.com/TeachuGis
- published: 21 May 2013
- views: 8019
Smartgeometry 2013 -- Day 1 Session 1 -- Constructing Data: Realizing Algorithm Presented by: Abdulmajid Karanouh - Aedas Research
The 2013 Smartgeometry Conference in London, UK focused on the theme of constructing for uncertainty.
About Smartgeometry Conference: This widely acclaimed conference is the world's premier event focused on computational and parametric design tools, technologies, and methodologies that allow and encourage new forms of architectural and structural expression. Learn more: http://bit.ly/19PAEw2
About GenerativeComponents: GenerativeComponents (GC) enables designers to explore more possibilities, in less time, create better designs, and efficiently create and manage complex geometric relationships. Learn more: http://bit.ly/17Pev0o
About Bentley Systems: Bentley is the global leader dedicated to providing architects, engineers, geospatial professionals, constructors, and owner-operators with comprehensive software solutions for sustaining infrastructure. Learn more: http://bit.ly/13m4WDd
- published: 09 Jul 2013
- views: 926
By Julie Goldberg
Julie Goldberg is the co-founder and chief engineer behind Empower Engine, a 2-person start-up that maps electoral campaign data for Democrats and their allies. She’ll talk a bit about the domain and a lot about some technical challenges she faced, mostly solved by caching. The system is built using Django, Leaflet, Tilestache and Postgres.
Campaigns organize geographically, so maps are very important. Organizers have their own turf, and it’s important to see how things vary in just their local area. Some data is static, but other data must be recomputed every day. We never know when a user will ask for a map of a specific data set in some small city or a specific organizer’s turf.
Geometric queries are much slower than most database queries, especially when you have hundreds of thousands of geometries. Empower Engine never does geometric calculations during a single web request, and the system minimizes geometric queries as much as possible. District overlaps are pre-computed, and person-district lookups are recomputed daily (using the previous day’s data it hasn’t changed). Shapes are stored in a separate table from other information about the district, so Postgres can store the district table efficiently. We have a custom wrapper around Tilestache to allow one configuration file to handle tons of maps. Tiles are only created on request and then stored them on S3. Every map has all its data in a single cache table to speed up Tilestache. The cache table is recreated and all tiles invalidated whenever the data or anything else about the map changes.
- published: 16 Mar 2015
- views: 300
Download file from “Highline BI 348 Class” section: https://people.highline.edu/mgirvin/excelisfun.htm
Learn about PowerPivot: Import Big Data, Build Data Model, Create Reports:
1) (00:04) Info about files for project
2) (00:20) Intro to Video and look at end result reports
3) (01:56) Overview of steps in building a Data Model in PowerPivot
4) (03:03) Look at source data files including text files with 5 million rows of records
5) (04:46) From Text Files, use Power Query to import 5 million rows of transactional sales data into the a Fact Table in the Data Model in PowerPivot
6) (09:48) From Excel file, use Power Query to import Product Retail and Standard Cost Dimension Table into the Data Model in PowerPivot
7) (10:52) Build Calendar Dimension Table in Excel and then use Power Query to import into the Data Model in PowerPivot
8) (13:44) From web get ISO 3166-1 County Code Data to build and then use Power Query to import Country Code Dimension Table into the Data Model in PowerPivot
9) (17:50) Build One-to-Many Relationships between Fact Table and Dimension Tables
10) (20:16) Build DAX Calculated Column for Net Revenue for each transaction. See the DAX functions: RELATED (Like VLOOKUP in Excel) and ROUND.
11) (22:30) Build DAX Measure (Calculated Field) for Total Net Revenue (Overall Total Net Revenue for entire Fact table). See the DAX function: SUM.
12) (24:16) Build PivotTable to see that the relationships are working and that we can pull fields from Dimension Tables and Fact Tables.
13) (24:16) Build PivotTable Report to show Net Revenue for each Country.
14) (25:33) “Trouble Shooting” Part of Example: Tracking Down Error between web site data and company data, including finding error and updating Country Code Excel Table and refreshing the linked table in the Data Model so that the PivotTable report updates and has correct results.
15) (30:35) Hide Fields from Field Lists using “Hide From Client Tools”
16) (31:59) Build PivotTable to see that with a Data Model PivotTable you can NOT group Dates
17) (33:20) Create DAX Calculated Column for Month Number and Month Name. See the DAX functions: MONTH and FORMAT (Like TEXT in Excel).
18) (35:20) Build PivotTable to see that Month Name does NOT sort correctly in a Data Model PivotTable.
19) (35:52) Learn how to Sort Month Name column by Month Number so that Month Name sorts correctly in a data Model PivotTable
20) (36:36) Build relationship between Calendar Table and Fact Table
21) (36:36) Build PivotTable Report to show Net Revenue by Month
22) (37:33) Create DAX Calculated Column for Year. See the DAX function: YEAR.
23) (38:02) Build PivotTable Report to show Net Revenue by Month & Year
24) (39:10) Build DAX Calculated Column for COGS for each transaction. See the DAX functions: RELATED (Like VLOOKUP in Excel) and ROUND.
25) (40:55) Build DAX Measure (Calculated Field) for Total COGS (Overall Total COGS for entire Fact table). See the DAX function: SUM.
26) (41:29) Build DAX Measure (Calculated Field) for Gross Profit using Measures (Calculated Fields) in our DAX formula.
27) (42:21) Build PivotTable Report to show Net Revenue, COGS and Gross Profit for each Year and Month.
28) (44:04) Build PivotTable Report to show Percentage Change for Net Revenue over Same Period Last Year.
29) (45:00) Build DAX Measure (Calculated Field) for Percentage Change over Same Period Last Year using the DAX functions: CALCULATE, SAMEPERIODLASTYEAR and IF.
30) (52:22) Build PivotTable Report to show Percentage of Grand Total for Each Product. Concept behind the formula.
31) (54:11) Build DAX Measure (Calculated Field) for Percentage of Grand Total using the DAX functions: CALCULATE and ALL.
32) (56:45) Refresh Reports when source data changes. In our example we bring 7 million rows into the Excel PowerPivot Data Model.
33) (57:48) Update Calendar table
34) (59:34) Summary and Conclusion.
Download Excel File Not: After clicking on link, Use Ctrl + F (Find) and search for “Highline BI 348 Class” or for the file name as seen at the beginning of the video.
More about CALCULATE function: https://www.youtube.com/watch?v=kMMohkVk8Ds
PowerPivot Playlist: https://www.youtube.com/playlist?list=PLrRPvpgDmw0nGCx21PRFbsJpUIH06LKs-
- published: 29 Oct 2015
- views: 7343
Project Page: http://vishnu.boddeti.net/projects/detection-by-synthesis.html
CVPR 2015 Paper Title:
Learning Scene-Specific Pedestrian Detectors without Real Data
Authors:
Hironori Hattori, Vishnu Naresh Boddeti, Kris Kitani, Takeo Kanade
Abstract:
We consider the problem of designing a scene-specific pedestrian detector in a scenario where we have zero instances of real pedestrian data (i.e., no labeled real data or unsupervised real data). This scenario may arise when a new surveillance system is installed in a novel location and a scene-specific pedestrian detector must be trained prior to any observations of pedestrians. The key idea of our approach is to infer the potential appearance of pedestrians using geometric scene data and a customizable database of virtual simulations of pedestrian motion. We propose an efficient discriminative learning method that generates a spatially-varying pedestrian appearance model that takes into the account the perspective geometry of the scene. As a result, our method is able to learn a unique pedestrian classifier customized for every possible location in the scene. Our experimental results show that our proposed approach outperforms classical pedestrian detection models and hybrid synthetic-real models. Our results also yield a surprising result, that our method using purely synthetic data is able to outperform models trained on real scene-specific data when data is limited.
- published: 13 Apr 2015
- views: 989
A city is a relatively large and permanent settlement. Although there is no agreement on how a city is distinguished from a town within general English language meanings, many cities have a particular administrative, legal, or historical status based on local law.
For example, in the U.S. state of Massachusetts an article of incorporation approved by the local state legislature distinguishes a city government from a town. In the United Kingdom and parts of the Commonwealth of Nations, a city is traditionally a settlement with a royal charter. Historically, in Europe, a city was understood to be an urban settlement with a cathedral.
Cities generally have complex systems for sanitation, utilities, land usage, housing, and transportation. The concentration of development greatly facilitates interaction between people and businesses, benefiting both parties in the process. A big city or metropolis usually has associated suburbs and exurbs. Such cities are usually associated with metropolitan areas and urban areas, creating numerous business commuters traveling to urban centers for employment. Once a city expands far enough to reach another city, this region can be deemed a conurbation or megalopolis.
This page contains text from Wikipedia, the Free Encyclopedia -
http://en.wikipedia.org/wiki/City
This article is licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License, which means that you can copy and modify it as long as the entire work (including additions) remains under this license.
This article is licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License, which means that you can copy and modify it as long as the entire work (including additions) remains under this license.
Data (
/ˈdeɪtə/ DAY-tə, /ˈdætə/ DA-tə, or /ˈdɑːtə/ DAH-tə) are values of qualitative or quantitative variables, belonging to a set of items. Data in computing (or data processing) are often represented by a combination of items organized in rows and multiple variables organized in columns. Data are typically the results of measurements and can be visualised using graphs or images. Data as an abstract concept can be viewed as the lowest level of abstraction from which information and then knowledge are derived. Raw data, i.e., unprocessed data, refers to a collection of numbers, characters and is a relative term; data processing commonly occurs by stages, and the "processed data" from one stage may be considered the "raw data" of the next. Field data refers to raw data collected in an uncontrolled in situ environment. Experimental data refers to data generated within the context of a scientific investigation by observation and recording.
The word data is the plural of datum, neuter past participle of the Latin dare, "to give", hence "something given". In discussions of problems in geometry, mathematics, engineering, and so on, the terms givens and data are used interchangeably. Such usage is the origin of data as a concept in computer science or data processing: data are numbers, words, images, etc., accepted as they stand.
This page contains text from Wikipedia, the Free Encyclopedia -
http://en.wikipedia.org/wiki/Data
This article is licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License, which means that you can copy and modify it as long as the entire work (including additions) remains under this license.
This article is licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License, which means that you can copy and modify it as long as the entire work (including additions) remains under this license.
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22:31Can A.I. Become More Human? // Gary Marcus, Geometric Intelligence (Hosted by FirstMark Capital)
Can A.I. Become More Human? // Gary Marcus, Geometric Intelligence (Hosted by FirstMark Capital)Can A.I. Become More Human? // Gary Marcus, Geometric Intelligence (Hosted by FirstMark Capital)
Gary Marcus, CEO at Geometric Intelligence, presented at FirstMark's Data Driven NYC on January 19, 2016. Marcus' talk focused on ways A.I. is stuck. Geometric Intelligence is redefining the boundaries of machine learning through innovative, patent-pending techniques that learn more efficiently from less data. Data Driven NYC is a monthly event covering Big Data and data-driven products and startups, hosted by Matt Turck, partner at FirstMark. FirstMark is an early stage venture capital firm based in New York City. Find out more about Data Driven NYC at http://datadrivennyc.com and FirstMark Capital at http://firstmarkcap.com. -
6:48X68K MDX FM音源64音（OPM*8個）+ PCM12音 RAYSTORM GEOMETRIC CITY+おまけ（1080p）
X68K MDX FM音源64音（OPM*8個）+ PCM12音 RAYSTORM GEOMETRIC CITY+おまけ（1080p）X68K MDX FM音源64音（OPM*8個）+ PCM12音 RAYSTORM GEOMETRIC CITY+おまけ（1080p）
フルHD動画です。 ※ヘッドフォン推奨。 FM64音+PCM12音使って本気で作ってみました。 後半はオマケ曲です。 8音ずつのMDXファイルを8つ作って同期再生させる事でFM64音を実現しています。 外部エフェクターの類は一切使用せず、FM音源によるデチューンと音量ディレイのみです。 PCMはドラムとボイスにのみ使用しています。 普段はニコ動に投稿しています。 これまでに投稿した動画はこちら（ニコ動）→http://www.nicovideo.jp/mylist/31231343 この動画のメイキングはこちら（ニコ動）→http://www.nicovideo.jp/watch/sm19191157 -
12:50David Moloney, CTO Movidius. Drones, Data X Conference 2015
David Moloney, CTO Movidius. Drones, Data X Conference 2015David Moloney, CTO Movidius. Drones, Data X Conference 2015
Movidius CTO David Moloney talks about the future of pilot-less drones thanks to machine vision and artificial intelligence. Also discussed is a new approach to storing 3D volumetric data at a tenth of the current size, paving way to a future where flying robots can autonomously obtain and act upon city-sized amounts of geometric data. Learn more about how machine vision is revolutionizing our devices at www.movidius.com -
1:19The Data Center Mural Project: Painting a Cloud
The Data Center Mural Project: Painting a CloudThe Data Center Mural Project: Painting a Cloud
The St. Ghislain, Belgium data center wall goes from a blank canvas to a colorful mural by Oli-B in this timelapse video. See more of Oli-B’s murals at g.co/datacentermurals -
79:363. Geometric Structures I
3. Geometric Structures I3. Geometric Structures I
MIT 6.851 Advanced Data Structures, Spring 2012 View the complete course: http://ocw.mit.edu/6-851S12 Instructor: Erik Demaine Point location and range searching: persistence, retroactivity, dynamization of augmentation through weight balance, and fractional cascading. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu -
61:15Data Mining (Advanced Analytics): Sparse Learning & Dimension Reduction
Data Mining (Advanced Analytics): Sparse Learning & Dimension ReductionData Mining (Advanced Analytics): Sparse Learning & Dimension Reduction
For tips on how to plan successful data mining projects, read this IBM white paper: http://bit.ly/1mDN62O More info at https://www.analyticszone.com/blogs/48ab01ca-89ca-4827-890f-f202be87cd81/entry/analytics_education_series_an_introduction?lang=en_us Aurélie C. Lozano will cover model selection, factor analysis, PCA., joint feature selection and estimation, Lasso and more. Key discussion points covered in this webcast will be: Analytics Capacities Landscape Why Dimension Reduction? - Solution: Dimension Reduction - Example: Document classification Methods for Dimension Reduction and Applications - Main classes of techniques for Dimension Reduction - Feature Extraction/Reduction - Unsupervised Feature Reduction - PCA - Geometric view of PCA: 2-D Gaussian Scatter plot - Example of Application for PCA: Clustering - Non-linear PCA - Multidimensional Scaling (MDS) - Manifold Learning - Manifold Learning: ISOMAP - Example of Application: Hand - Supervised Feature Reduction - Linear Discriminant Analysis (LDA) - LDA: Sample applications - Supervised Principal Components - Supervised PCA: Example of Application - Feature/variable selection - Feature Selection - Feature Selection: Filter Methods - Feature Selection: Wrapper Methods - Sample Algorithms - Sample Applications - Sparse Learning - Why Sparse Learning? - Sparse Learning: Joint dimension reduction and estimation - The Lasso: The most popular sparse learning method - The Group Lasso: Extends the lasso to accommodate grouped selection - Various types of sparsity on matrices - Various types of sparsity (matrix factorization) - Application to GWAS - Genetic Basis of Complex Diseases - Application to climate change attribution - The approach: Sparse Learning with spatio-temporal data - Application: Key influencers in online communities - Application: IBM Lotus Bloggers - Sparse Learning on Matrices: Image denoising - Sparse Learning on matrices: network inference -
1:01ArcMap 10: Calculating Area by Boundary using Calculate Geometry (basic map calculation)
ArcMap 10: Calculating Area by Boundary using Calculate Geometry (basic map calculation)ArcMap 10: Calculating Area by Boundary using Calculate Geometry (basic map calculation)
It's all about calculating the area for a defined boundary. One advantage with GIS is the ability to define boundaries, thus allowing us to Calculate Geometry. In addition to basic calculations, the ArcMap program can also standardize area in metric and empirical units. The Toronto Bikeway, Parks and Regional Boundaries were attained from the Toronto Open Data. Video contains public sector Datasets made available under the City of Toronto's Open Data Licence v2.0. http://www1.toronto.ca/wps/portal/open_data/open_data_fact_sheet_details?vgnextoid=59986aa8cc819210VgnVCM10000067d60f89RCRD Visit my blog for more information: http://teachugis.wordpress.com/ -
4:25ArcMap 10: How to delineate a watershed using a DEM: Calculate geometry (4 of 4)
ArcMap 10: How to delineate a watershed using a DEM: Calculate geometry (4 of 4)ArcMap 10: How to delineate a watershed using a DEM: Calculate geometry (4 of 4)
Thanks for joining. Sorry I didn't download a stream dataset. This video will only focus on calculate geometry. 4 of 4. TheRegional Boundary was attained from the Toronto Open Data and the DEM a 1:50,000 attained from GeoBase. Video contains public sector Datasets made available under the City of Toronto's Open Data Licence v2.0 and GeoBase® (http://www.geobase.ca/). Visit twitter feed for more information: https://twitter.com/TeachuGis -
20:47Smartgeometry 2013 -- Constructing Data: Realizing Algorithm
Smartgeometry 2013 -- Constructing Data: Realizing AlgorithmSmartgeometry 2013 -- Constructing Data: Realizing Algorithm
Smartgeometry 2013 -- Day 1 Session 1 -- Constructing Data: Realizing Algorithm Presented by: Abdulmajid Karanouh - Aedas Research The 2013 Smartgeometry Conference in London, UK focused on the theme of constructing for uncertainty. About Smartgeometry Conference: This widely acclaimed conference is the world's premier event focused on computational and parametric design tools, technologies, and methodologies that allow and encourage new forms of architectural and structural expression. Learn more: http://bit.ly/19PAEw2 About GenerativeComponents: GenerativeComponents (GC) enables designers to explore more possibilities, in less time, create better designs, and efficiently create and manage complex geometric relationships. Learn more: http://bit.ly/17Pev0o About Bentley Systems: Bentley is the global leader dedicated to providing architects, engineers, geospatial professionals, constructors, and owner-operators with comprehensive software solutions for sustaining infrastructure. Learn more: http://bit.ly/13m4WDd -
43:07Caching is Your Friend - Creating Tons of Maps on the Fly and Auto-Updating Some
Caching is Your Friend - Creating Tons of Maps on the Fly and Auto-Updating SomeCaching is Your Friend - Creating Tons of Maps on the Fly and Auto-Updating Some
By Julie Goldberg Julie Goldberg is the co-founder and chief engineer behind Empower Engine, a 2-person start-up that maps electoral campaign data for Democrats and their allies. She’ll talk a bit about the domain and a lot about some technical challenges she faced, mostly solved by caching. The system is built using Django, Leaflet, Tilestache and Postgres. Campaigns organize geographically, so maps are very important. Organizers have their own turf, and it’s important to see how things vary in just their local area. Some data is static, but other data must be recomputed every day. We never know when a user will ask for a map of a specific data set in some small city or a specific organizer’s turf. Geometric queries are much slower than most database queries, especially when you have hundreds of thousands of geometries. Empower Engine never does geometric calculations during a single web request, and the system minimizes geometric queries as much as possible. District overlaps are pre-computed, and person-district lookups are recomputed daily (using the previous day’s data it hasn’t changed). Shapes are stored in a separate table from other information about the district, so Postgres can store the district table efficiently. We have a custom wrapper around Tilestache to allow one configuration file to handle tons of maps. Tiles are only created on request and then stored them on S3. Every map has all its data in a single cache table to speed up Tilestache. The cache table is recreated and all tiles invalidated whenever the data or anything else about the map changes. -
61:05Basic Excel Business Analytics #42: Comprehensive PowerPivot, Data Model, DAX & Reporting Example
Basic Excel Business Analytics #42: Comprehensive PowerPivot, Data Model, DAX & Reporting ExampleBasic Excel Business Analytics #42: Comprehensive PowerPivot, Data Model, DAX & Reporting Example
Download file from “Highline BI 348 Class” section: https://people.highline.edu/mgirvin/excelisfun.htm Learn about PowerPivot: Import Big Data, Build Data Model, Create Reports: 1) (00:04) Info about files for project 2) (00:20) Intro to Video and look at end result reports 3) (01:56) Overview of steps in building a Data Model in PowerPivot 4) (03:03) Look at source data files including text files with 5 million rows of records 5) (04:46) From Text Files, use Power Query to import 5 million rows of transactional sales data into the a Fact Table in the Data Model in PowerPivot 6) (09:48) From Excel file, use Power Query to import Product Retail and Standard Cost Dimension Table into the Data Model in PowerPivot 7) (10:52) Build Calendar Dimension Table in Excel and then use Power Query to import into the Data Model in PowerPivot 8) (13:44) From web get ISO 3166-1 County Code Data to build and then use Power Query to import Country Code Dimension Table into the Data Model in PowerPivot 9) (17:50) Build One-to-Many Relationships between Fact Table and Dimension Tables 10) (20:16) Build DAX Calculated Column for Net Revenue for each transaction. See the DAX functions: RELATED (Like VLOOKUP in Excel) and ROUND. 11) (22:30) Build DAX Measure (Calculated Field) for Total Net Revenue (Overall Total Net Revenue for entire Fact table). See the DAX function: SUM. 12) (24:16) Build PivotTable to see that the relationships are working and that we can pull fields from Dimension Tables and Fact Tables. 13) (24:16) Build PivotTable Report to show Net Revenue for each Country. 14) (25:33) “Trouble Shooting” Part of Example: Tracking Down Error between web site data and company data, including finding error and updating Country Code Excel Table and refreshing the linked table in the Data Model so that the PivotTable report updates and has correct results. 15) (30:35) Hide Fields from Field Lists using “Hide From Client Tools” 16) (31:59) Build PivotTable to see that with a Data Model PivotTable you can NOT group Dates 17) (33:20) Create DAX Calculated Column for Month Number and Month Name. See the DAX functions: MONTH and FORMAT (Like TEXT in Excel). 18) (35:20) Build PivotTable to see that Month Name does NOT sort correctly in a Data Model PivotTable. 19) (35:52) Learn how to Sort Month Name column by Month Number so that Month Name sorts correctly in a data Model PivotTable 20) (36:36) Build relationship between Calendar Table and Fact Table 21) (36:36) Build PivotTable Report to show Net Revenue by Month 22) (37:33) Create DAX Calculated Column for Year. See the DAX function: YEAR. 23) (38:02) Build PivotTable Report to show Net Revenue by Month & Year 24) (39:10) Build DAX Calculated Column for COGS for each transaction. See the DAX functions: RELATED (Like VLOOKUP in Excel) and ROUND. 25) (40:55) Build DAX Measure (Calculated Field) for Total COGS (Overall Total COGS for entire Fact table). See the DAX function: SUM. 26) (41:29) Build DAX Measure (Calculated Field) for Gross Profit using Measures (Calculated Fields) in our DAX formula. 27) (42:21) Build PivotTable Report to show Net Revenue, COGS and Gross Profit for each Year and Month. 28) (44:04) Build PivotTable Report to show Percentage Change for Net Revenue over Same Period Last Year. 29) (45:00) Build DAX Measure (Calculated Field) for Percentage Change over Same Period Last Year using the DAX functions: CALCULATE, SAMEPERIODLASTYEAR and IF. 30) (52:22) Build PivotTable Report to show Percentage of Grand Total for Each Product. Concept behind the formula. 31) (54:11) Build DAX Measure (Calculated Field) for Percentage of Grand Total using the DAX functions: CALCULATE and ALL. 32) (56:45) Refresh Reports when source data changes. In our example we bring 7 million rows into the Excel PowerPivot Data Model. 33) (57:48) Update Calendar table 34) (59:34) Summary and Conclusion. Download Excel File Not: After clicking on link, Use Ctrl + F (Find) and search for “Highline BI 348 Class” or for the file name as seen at the beginning of the video. More about CALCULATE function: https://www.youtube.com/watch?v=kMMohkVk8Ds PowerPivot Playlist: https://www.youtube.com/playlist?list=PLrRPvpgDmw0nGCx21PRFbsJpUIH06LKs- -
2:12Learning Scene-Specific Pedestrian Detectors without Real Data, CVPR 2015
Learning Scene-Specific Pedestrian Detectors without Real Data, CVPR 2015Learning Scene-Specific Pedestrian Detectors without Real Data, CVPR 2015
Project Page: http://vishnu.boddeti.net/projects/detection-by-synthesis.html CVPR 2015 Paper Title: Learning Scene-Specific Pedestrian Detectors without Real Data Authors: Hironori Hattori, Vishnu Naresh Boddeti, Kris Kitani, Takeo Kanade Abstract: We consider the problem of designing a scene-specific pedestrian detector in a scenario where we have zero instances of real pedestrian data (i.e., no labeled real data or unsupervised real data). This scenario may arise when a new surveillance system is installed in a novel location and a scene-specific pedestrian detector must be trained prior to any observations of pedestrians. The key idea of our approach is to infer the potential appearance of pedestrians using geometric scene data and a customizable database of virtual simulations of pedestrian motion. We propose an efficient discriminative learning method that generates a spatially-varying pedestrian appearance model that takes into the account the perspective geometry of the scene. As a result, our method is able to learn a unique pedestrian classifier customized for every possible location in the scene. Our experimental results show that our proposed approach outperforms classical pedestrian detection models and hybrid synthetic-real models. Our results also yield a surprising result, that our method using purely synthetic data is able to outperform models trained on real scene-specific data when data is limited.
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Can A.I. Become More Human? // Gary Marcus, Geometric Intelligence (Hosted by FirstMark Capital)
Gary Marcus, CEO at Geometric Intelligence, presented at FirstMark's Data Driven NYC on January 19, 2016. Marcus' talk focused on ways A.I. is stuck. Geometric Intelligence is redefining the boundaries of machine learning through innovative, patent-pending techniques that learn more efficiently from less data. Data Driven NYC is a monthly event covering Big Data and data-driven products and startups, hosted by Matt Turck, partner at FirstMark. FirstMark is an early stage venture capital firm based in New York City. Find out more about Data Driven NYC at http://datadrivennyc.com and FirstMark Capital at http://firstmarkcap.com. -
X68K MDX FM音源64音（OPM*8個）+ PCM12音 RAYSTORM GEOMETRIC CITY+おまけ（1080p）
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David Moloney, CTO Movidius. Drones, Data X Conference 2015
Movidius CTO David Moloney talks about the future of pilot-less drones thanks to machine vision and artificial intelligence. Also discussed is a new approach to storing 3D volumetric data at a tenth of the current size, paving way to a future where flying robots can autonomously obtain and act upon city-sized amounts of geometric data. Learn more about how machine vision is revolutionizing our devices at www.movidius.com -
The Data Center Mural Project: Painting a Cloud
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3. Geometric Structures I
MIT 6.851 Advanced Data Structures, Spring 2012 View the complete course: http://ocw.mit.edu/6-851S12 Instructor: Erik Demaine Point location and range searching: persistence, retroactivity, dynamization of augmentation through weight balance, and fractional cascading. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu -
Data Mining (Advanced Analytics): Sparse Learning & Dimension Reduction
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ArcMap 10: Calculating Area by Boundary using Calculate Geometry (basic map calculation)
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ArcMap 10: How to delineate a watershed using a DEM: Calculate geometry (4 of 4)
Thanks for joining. Sorry I didn't download a stream dataset. This video will only focus on calculate geometry. 4 of 4. TheRegional Boundary was attained from the Toronto Open Data and the DEM a 1:50,000 attained from GeoBase. Video contains public sector Datasets made available under the City of Toronto's Open Data Licence v2.0 and GeoBase® (http://www.geobase.ca/). Visit twitter feed for more information: https://twitter.com/TeachuGis -
Smartgeometry 2013 -- Constructing Data: Realizing Algorithm
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Caching is Your Friend - Creating Tons of Maps on the Fly and Auto-Updating Some
By Julie Goldberg Julie Goldberg is the co-founder and chief engineer behind Empower Engine, a 2-person start-up that maps electoral campaign data for Democrats and their allies. She’ll talk a bit about the domain and a lot about some technical challenges she faced, mostly solved by caching. The system is built using Django, Leaflet, Tilestache and Postgres. Campaigns organize geographically, so maps are very important. Organizers have their own turf, and it’s important to see how things vary in just their local area. Some data is static, but other data must be recomputed every day. We never know when a user will ask for a map of a specific data set in some small city or a specific organizer’s turf. Geometric queries are much slower than most database queries, especially when you have h... -
Basic Excel Business Analytics #42: Comprehensive PowerPivot, Data Model, DAX & Reporting Example
Download file from “Highline BI 348 Class” section: https://people.highline.edu/mgirvin/excelisfun.htm Learn about PowerPivot: Import Big Data, Build Data Model, Create Reports: 1) (00:04) Info about files for project 2) (00:20) Intro to Video and look at end result reports 3) (01:56) Overview of steps in building a Data Model in PowerPivot 4) (03:03) Look at source data files including text files with 5 million rows of records 5) (04:46) From Text Files, use Power Query to import 5 million rows of transactional sales data into the a Fact Table in the Data Model in PowerPivot 6) (09:48) From Excel file, use Power Query to import Product Retail and Standard Cost Dimension Table into the Data Model in PowerPivot 7) (10:52) Build Calendar Dimension Table in Excel and then use Power Query to i... -
Learning Scene-Specific Pedestrian Detectors without Real Data, CVPR 2015
Project Page: http://vishnu.boddeti.net/projects/detection-by-synthesis.html CVPR 2015 Paper Title: Learning Scene-Specific Pedestrian Detectors without Real Data Authors: Hironori Hattori, Vishnu Naresh Boddeti, Kris Kitani, Takeo Kanade Abstract: We consider the problem of designing a scene-specific pedestrian detector in a scenario where we have zero instances of real pedestrian data (i.e., no labeled real data or unsupervised real data). This scenario may arise when a new surveillance system is installed in a novel location and a scene-specific pedestrian detector must be trained prior to any observations of pedestrians. The key idea of our approach is to infer the potential appearance of pedestrians using geometric scene data and a customizable database of virtual simulations of pe...
Can A.I. Become More Human? // Gary Marcus, Geometric Intelligence (Hosted by FirstMark Capital)
- Order: Reorder
- Duration: 22:31
- Updated: 25 Jan 2016
- views: 2152
Gary Marcus, CEO at Geometric Intelligence, presented at FirstMark's Data Driven NYC on January 19, 2016. Marcus' talk focused on ways A.I. is stuck.
Geometric...
Gary Marcus, CEO at Geometric Intelligence, presented at FirstMark's Data Driven NYC on January 19, 2016. Marcus' talk focused on ways A.I. is stuck.
Geometric Intelligence is redefining the boundaries of machine learning through innovative, patent-pending techniques that learn more efficiently from less data.
Data Driven NYC is a monthly event covering Big Data and data-driven products and startups, hosted by Matt Turck, partner at FirstMark.
FirstMark is an early stage venture capital firm based in New York City. Find out more about Data Driven NYC at http://datadrivennyc.com and FirstMark Capital at http://firstmarkcap.com.
wn.com/Can A.I. Become More Human Gary Marcus, Geometric Intelligence (Hosted By Firstmark Capital)
Gary Marcus, CEO at Geometric Intelligence, presented at FirstMark's Data Driven NYC on January 19, 2016. Marcus' talk focused on ways A.I. is stuck.
Geometric Intelligence is redefining the boundaries of machine learning through innovative, patent-pending techniques that learn more efficiently from less data.
Data Driven NYC is a monthly event covering Big Data and data-driven products and startups, hosted by Matt Turck, partner at FirstMark.
FirstMark is an early stage venture capital firm based in New York City. Find out more about Data Driven NYC at http://datadrivennyc.com and FirstMark Capital at http://firstmarkcap.com.
- published: 25 Jan 2016
- views: 2152
X68K MDX FM音源64音（OPM*8個）+ PCM12音 RAYSTORM GEOMETRIC CITY+おまけ（1080p）
- Order: Reorder
- Duration: 6:48
- Updated: 23 Oct 2012
- views: 16814
フルHD動画です。
※ヘッドフォン推奨。
FM64音+PCM12音使って本気で作ってみました。
後半はオマケ曲です。
8音ずつのMDXファイルを8つ作って同期再生させる事でFM64音を実現しています。
外部エフェクターの類は一切使用せず、FM音源によるデチューンと音量ディレイのみです。
PCMはドラムとボイスにのみ使...
フルHD動画です。
※ヘッドフォン推奨。
FM64音+PCM12音使って本気で作ってみました。
後半はオマケ曲です。
8音ずつのMDXファイルを8つ作って同期再生させる事でFM64音を実現しています。
外部エフェクターの類は一切使用せず、FM音源によるデチューンと音量ディレイのみです。
PCMはドラムとボイスにのみ使用しています。
普段はニコ動に投稿しています。
これまでに投稿した動画はこちら（ニコ動）→http://www.nicovideo.jp/mylist/31231343
この動画のメイキングはこちら（ニコ動）→http://www.nicovideo.jp/watch/sm19191157
wn.com/X68K Mdx Fm音源64音（Opm 8個） Pcm12音 Raystorm Geometric City おまけ（1080P）
フルHD動画です。
※ヘッドフォン推奨。
FM64音+PCM12音使って本気で作ってみました。
後半はオマケ曲です。
8音ずつのMDXファイルを8つ作って同期再生させる事でFM64音を実現しています。
外部エフェクターの類は一切使用せず、FM音源によるデチューンと音量ディレイのみです。
PCMはドラムとボイスにのみ使用しています。
普段はニコ動に投稿しています。
これまでに投稿した動画はこちら（ニコ動）→http://www.nicovideo.jp/mylist/31231343
この動画のメイキングはこちら（ニコ動）→http://www.nicovideo.jp/watch/sm19191157
- published: 23 Oct 2012
- views: 16814
David Moloney, CTO Movidius. Drones, Data X Conference 2015
- Order: Reorder
- Duration: 12:50
- Updated: 19 Nov 2015
- views: 573
Movidius CTO David Moloney talks about the future of pilot-less drones thanks to machine vision and artificial intelligence. Also discussed is a new approach to...
Movidius CTO David Moloney talks about the future of pilot-less drones thanks to machine vision and artificial intelligence. Also discussed is a new approach to storing 3D volumetric data at a tenth of the current size, paving way to a future where flying robots can autonomously obtain and act upon city-sized amounts of geometric data.
Learn more about how machine vision is revolutionizing our devices at www.movidius.com
wn.com/David Moloney, Cto Movidius. Drones, Data X Conference 2015
Movidius CTO David Moloney talks about the future of pilot-less drones thanks to machine vision and artificial intelligence. Also discussed is a new approach to storing 3D volumetric data at a tenth of the current size, paving way to a future where flying robots can autonomously obtain and act upon city-sized amounts of geometric data.
Learn more about how machine vision is revolutionizing our devices at www.movidius.com
- published: 19 Nov 2015
- views: 573
The Data Center Mural Project: Painting a Cloud
- Order: Reorder
- Duration: 1:19
- Updated: 12 May 2016
- views: 2219
The St. Ghislain, Belgium data center wall goes from a blank canvas to a colorful mural by Oli-B in this timelapse video. See more of Oli-B’s murals at g.co/dat...
The St. Ghislain, Belgium data center wall goes from a blank canvas to a colorful mural by Oli-B in this timelapse video. See more of Oli-B’s murals at g.co/datacentermurals
wn.com/The Data Center Mural Project Painting A Cloud
The St. Ghislain, Belgium data center wall goes from a blank canvas to a colorful mural by Oli-B in this timelapse video. See more of Oli-B’s murals at g.co/datacentermurals
- published: 12 May 2016
- views: 2219
3. Geometric Structures I
- Order: Reorder
- Duration: 79:36
- Updated: 18 Dec 2013
- views: 7855
MIT 6.851 Advanced Data Structures, Spring 2012
View the complete course: http://ocw.mit.edu/6-851S12
Instructor: Erik Demaine
Point location and range searchi...
MIT 6.851 Advanced Data Structures, Spring 2012
View the complete course: http://ocw.mit.edu/6-851S12
Instructor: Erik Demaine
Point location and range searching: persistence, retroactivity, dynamization of augmentation through weight balance, and fractional cascading.
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
wn.com/3. Geometric Structures I
MIT 6.851 Advanced Data Structures, Spring 2012
View the complete course: http://ocw.mit.edu/6-851S12
Instructor: Erik Demaine
Point location and range searching: persistence, retroactivity, dynamization of augmentation through weight balance, and fractional cascading.
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
- published: 18 Dec 2013
- views: 7855
Data Mining (Advanced Analytics): Sparse Learning & Dimension Reduction
- Order: Reorder
- Duration: 61:15
- Updated: 23 Jan 2014
- views: 3056
For tips on how to plan successful data mining projects, read this IBM white paper: http://bit.ly/1mDN62O
More info at https://www.analyticszone.com/blogs/48ab...
For tips on how to plan successful data mining projects, read this IBM white paper: http://bit.ly/1mDN62O
More info at https://www.analyticszone.com/blogs/48ab01ca-89ca-4827-890f-f202be87cd81/entry/analytics_education_series_an_introduction?lang=en_us
Aurélie C. Lozano will cover model selection, factor analysis, PCA., joint feature selection and estimation, Lasso and more. Key discussion points covered in this webcast will be:
Analytics Capacities Landscape
Why Dimension Reduction?
- Solution: Dimension Reduction
- Example: Document classification
Methods for Dimension Reduction and Applications
- Main classes of techniques for Dimension Reduction
- Feature Extraction/Reduction
- Unsupervised Feature Reduction
- PCA
- Geometric view of PCA: 2-D Gaussian Scatter plot
- Example of Application for PCA: Clustering
- Non-linear PCA
- Multidimensional Scaling (MDS)
- Manifold Learning
- Manifold Learning: ISOMAP
- Example of Application: Hand
- Supervised Feature Reduction
- Linear Discriminant Analysis (LDA)
- LDA: Sample applications
- Supervised Principal Components
- Supervised PCA: Example of Application
- Feature/variable selection
- Feature Selection
- Feature Selection: Filter Methods
- Feature Selection: Wrapper Methods
- Sample Algorithms
- Sample Applications
- Sparse Learning
- Why Sparse Learning?
- Sparse Learning: Joint dimension reduction and estimation
- The Lasso: The most popular sparse learning method
- The Group Lasso: Extends the lasso to accommodate grouped selection
- Various types of sparsity on matrices
- Various types of sparsity (matrix factorization)
- Application to GWAS - Genetic Basis of Complex Diseases
- Application to climate change attribution
- The approach: Sparse Learning with spatio-temporal data
- Application: Key influencers in online communities
- Application: IBM Lotus Bloggers
- Sparse Learning on Matrices: Image denoising
- Sparse Learning on matrices: network inference
wn.com/Data Mining (Advanced Analytics) Sparse Learning Dimension Reduction
For tips on how to plan successful data mining projects, read this IBM white paper: http://bit.ly/1mDN62O
More info at https://www.analyticszone.com/blogs/48ab01ca-89ca-4827-890f-f202be87cd81/entry/analytics_education_series_an_introduction?lang=en_us
Aurélie C. Lozano will cover model selection, factor analysis, PCA., joint feature selection and estimation, Lasso and more. Key discussion points covered in this webcast will be:
Analytics Capacities Landscape
Why Dimension Reduction?
- Solution: Dimension Reduction
- Example: Document classification
Methods for Dimension Reduction and Applications
- Main classes of techniques for Dimension Reduction
- Feature Extraction/Reduction
- Unsupervised Feature Reduction
- PCA
- Geometric view of PCA: 2-D Gaussian Scatter plot
- Example of Application for PCA: Clustering
- Non-linear PCA
- Multidimensional Scaling (MDS)
- Manifold Learning
- Manifold Learning: ISOMAP
- Example of Application: Hand
- Supervised Feature Reduction
- Linear Discriminant Analysis (LDA)
- LDA: Sample applications
- Supervised Principal Components
- Supervised PCA: Example of Application
- Feature/variable selection
- Feature Selection
- Feature Selection: Filter Methods
- Feature Selection: Wrapper Methods
- Sample Algorithms
- Sample Applications
- Sparse Learning
- Why Sparse Learning?
- Sparse Learning: Joint dimension reduction and estimation
- The Lasso: The most popular sparse learning method
- The Group Lasso: Extends the lasso to accommodate grouped selection
- Various types of sparsity on matrices
- Various types of sparsity (matrix factorization)
- Application to GWAS - Genetic Basis of Complex Diseases
- Application to climate change attribution
- The approach: Sparse Learning with spatio-temporal data
- Application: Key influencers in online communities
- Application: IBM Lotus Bloggers
- Sparse Learning on Matrices: Image denoising
- Sparse Learning on matrices: network inference
- published: 23 Jan 2014
- views: 3056
ArcMap 10: Calculating Area by Boundary using Calculate Geometry (basic map calculation)
- Order: Reorder
- Duration: 1:01
- Updated: 22 Apr 2013
- views: 1662
It's all about calculating the area for a defined boundary. One advantage with GIS is the ability to define boundaries, thus allowing us to Calculate Geometry....
It's all about calculating the area for a defined boundary. One advantage with GIS is the ability to define boundaries, thus allowing us to Calculate Geometry. In addition to basic calculations, the ArcMap program can also standardize area in metric and empirical units.
The Toronto Bikeway, Parks and Regional Boundaries were attained from the Toronto Open Data.
Video contains public sector Datasets made available under the City of Toronto's Open Data Licence v2.0.
http://www1.toronto.ca/wps/portal/open_data/open_data_fact_sheet_details?vgnextoid=59986aa8cc819210VgnVCM10000067d60f89RCRD
Visit my blog for more information: http://teachugis.wordpress.com/
wn.com/Arcmap 10 Calculating Area By Boundary Using Calculate Geometry (Basic Map Calculation)
It's all about calculating the area for a defined boundary. One advantage with GIS is the ability to define boundaries, thus allowing us to Calculate Geometry. In addition to basic calculations, the ArcMap program can also standardize area in metric and empirical units.
The Toronto Bikeway, Parks and Regional Boundaries were attained from the Toronto Open Data.
Video contains public sector Datasets made available under the City of Toronto's Open Data Licence v2.0.
http://www1.toronto.ca/wps/portal/open_data/open_data_fact_sheet_details?vgnextoid=59986aa8cc819210VgnVCM10000067d60f89RCRD
Visit my blog for more information: http://teachugis.wordpress.com/
- published: 22 Apr 2013
- views: 1662
ArcMap 10: How to delineate a watershed using a DEM: Calculate geometry (4 of 4)
- Order: Reorder
- Duration: 4:25
- Updated: 21 May 2013
- views: 8019
Thanks for joining. Sorry I didn't download a stream dataset. This video will only focus on calculate geometry. 4 of 4.
TheRegional Boundary was attained fro...
Thanks for joining. Sorry I didn't download a stream dataset. This video will only focus on calculate geometry. 4 of 4.
TheRegional Boundary was attained from the Toronto Open Data and the DEM a 1:50,000 attained from GeoBase.
Video contains public sector Datasets made available under the City of Toronto's Open Data Licence v2.0 and GeoBase® (http://www.geobase.ca/).
Visit twitter feed for more information:
https://twitter.com/TeachuGis
wn.com/Arcmap 10 How To Delineate A Watershed Using A Dem Calculate Geometry (4 Of 4)
Thanks for joining. Sorry I didn't download a stream dataset. This video will only focus on calculate geometry. 4 of 4.
TheRegional Boundary was attained from the Toronto Open Data and the DEM a 1:50,000 attained from GeoBase.
Video contains public sector Datasets made available under the City of Toronto's Open Data Licence v2.0 and GeoBase® (http://www.geobase.ca/).
Visit twitter feed for more information:
https://twitter.com/TeachuGis
- published: 21 May 2013
- views: 8019
Smartgeometry 2013 -- Constructing Data: Realizing Algorithm
- Order: Reorder
- Duration: 20:47
- Updated: 09 Jul 2013
- views: 926
Smartgeometry 2013 -- Day 1 Session 1 -- Constructing Data: Realizing Algorithm Presented by: Abdulmajid Karanouh - Aedas Research
The 2013 Smartgeometry Confe...
Smartgeometry 2013 -- Day 1 Session 1 -- Constructing Data: Realizing Algorithm Presented by: Abdulmajid Karanouh - Aedas Research
The 2013 Smartgeometry Conference in London, UK focused on the theme of constructing for uncertainty.
About Smartgeometry Conference: This widely acclaimed conference is the world's premier event focused on computational and parametric design tools, technologies, and methodologies that allow and encourage new forms of architectural and structural expression. Learn more: http://bit.ly/19PAEw2
About GenerativeComponents: GenerativeComponents (GC) enables designers to explore more possibilities, in less time, create better designs, and efficiently create and manage complex geometric relationships. Learn more: http://bit.ly/17Pev0o
About Bentley Systems: Bentley is the global leader dedicated to providing architects, engineers, geospatial professionals, constructors, and owner-operators with comprehensive software solutions for sustaining infrastructure. Learn more: http://bit.ly/13m4WDd
wn.com/Smartgeometry 2013 Constructing Data Realizing Algorithm
Smartgeometry 2013 -- Day 1 Session 1 -- Constructing Data: Realizing Algorithm Presented by: Abdulmajid Karanouh - Aedas Research
The 2013 Smartgeometry Conference in London, UK focused on the theme of constructing for uncertainty.
About Smartgeometry Conference: This widely acclaimed conference is the world's premier event focused on computational and parametric design tools, technologies, and methodologies that allow and encourage new forms of architectural and structural expression. Learn more: http://bit.ly/19PAEw2
About GenerativeComponents: GenerativeComponents (GC) enables designers to explore more possibilities, in less time, create better designs, and efficiently create and manage complex geometric relationships. Learn more: http://bit.ly/17Pev0o
About Bentley Systems: Bentley is the global leader dedicated to providing architects, engineers, geospatial professionals, constructors, and owner-operators with comprehensive software solutions for sustaining infrastructure. Learn more: http://bit.ly/13m4WDd
- published: 09 Jul 2013
- views: 926
Caching is Your Friend - Creating Tons of Maps on the Fly and Auto-Updating Some
- Order: Reorder
- Duration: 43:07
- Updated: 16 Mar 2015
- views: 300
By Julie Goldberg
Julie Goldberg is the co-founder and chief engineer behind Empower Engine, a 2-person start-up that maps electoral campaign data for Democrat...
By Julie Goldberg
Julie Goldberg is the co-founder and chief engineer behind Empower Engine, a 2-person start-up that maps electoral campaign data for Democrats and their allies. She’ll talk a bit about the domain and a lot about some technical challenges she faced, mostly solved by caching. The system is built using Django, Leaflet, Tilestache and Postgres.
Campaigns organize geographically, so maps are very important. Organizers have their own turf, and it’s important to see how things vary in just their local area. Some data is static, but other data must be recomputed every day. We never know when a user will ask for a map of a specific data set in some small city or a specific organizer’s turf.
Geometric queries are much slower than most database queries, especially when you have hundreds of thousands of geometries. Empower Engine never does geometric calculations during a single web request, and the system minimizes geometric queries as much as possible. District overlaps are pre-computed, and person-district lookups are recomputed daily (using the previous day’s data it hasn’t changed). Shapes are stored in a separate table from other information about the district, so Postgres can store the district table efficiently. We have a custom wrapper around Tilestache to allow one configuration file to handle tons of maps. Tiles are only created on request and then stored them on S3. Every map has all its data in a single cache table to speed up Tilestache. The cache table is recreated and all tiles invalidated whenever the data or anything else about the map changes.
wn.com/Caching Is Your Friend Creating Tons Of Maps On The Fly And Auto Updating Some
By Julie Goldberg
Julie Goldberg is the co-founder and chief engineer behind Empower Engine, a 2-person start-up that maps electoral campaign data for Democrats and their allies. She’ll talk a bit about the domain and a lot about some technical challenges she faced, mostly solved by caching. The system is built using Django, Leaflet, Tilestache and Postgres.
Campaigns organize geographically, so maps are very important. Organizers have their own turf, and it’s important to see how things vary in just their local area. Some data is static, but other data must be recomputed every day. We never know when a user will ask for a map of a specific data set in some small city or a specific organizer’s turf.
Geometric queries are much slower than most database queries, especially when you have hundreds of thousands of geometries. Empower Engine never does geometric calculations during a single web request, and the system minimizes geometric queries as much as possible. District overlaps are pre-computed, and person-district lookups are recomputed daily (using the previous day’s data it hasn’t changed). Shapes are stored in a separate table from other information about the district, so Postgres can store the district table efficiently. We have a custom wrapper around Tilestache to allow one configuration file to handle tons of maps. Tiles are only created on request and then stored them on S3. Every map has all its data in a single cache table to speed up Tilestache. The cache table is recreated and all tiles invalidated whenever the data or anything else about the map changes.
- published: 16 Mar 2015
- views: 300
Basic Excel Business Analytics #42: Comprehensive PowerPivot, Data Model, DAX & Reporting Example
- Order: Reorder
- Duration: 61:05
- Updated: 29 Oct 2015
- views: 7343
Download file from “Highline BI 348 Class” section: https://people.highline.edu/mgirvin/excelisfun.htm
Learn about PowerPivot: Import Big Data, Build Data Model...
Download file from “Highline BI 348 Class” section: https://people.highline.edu/mgirvin/excelisfun.htm
Learn about PowerPivot: Import Big Data, Build Data Model, Create Reports:
1) (00:04) Info about files for project
2) (00:20) Intro to Video and look at end result reports
3) (01:56) Overview of steps in building a Data Model in PowerPivot
4) (03:03) Look at source data files including text files with 5 million rows of records
5) (04:46) From Text Files, use Power Query to import 5 million rows of transactional sales data into the a Fact Table in the Data Model in PowerPivot
6) (09:48) From Excel file, use Power Query to import Product Retail and Standard Cost Dimension Table into the Data Model in PowerPivot
7) (10:52) Build Calendar Dimension Table in Excel and then use Power Query to import into the Data Model in PowerPivot
8) (13:44) From web get ISO 3166-1 County Code Data to build and then use Power Query to import Country Code Dimension Table into the Data Model in PowerPivot
9) (17:50) Build One-to-Many Relationships between Fact Table and Dimension Tables
10) (20:16) Build DAX Calculated Column for Net Revenue for each transaction. See the DAX functions: RELATED (Like VLOOKUP in Excel) and ROUND.
11) (22:30) Build DAX Measure (Calculated Field) for Total Net Revenue (Overall Total Net Revenue for entire Fact table). See the DAX function: SUM.
12) (24:16) Build PivotTable to see that the relationships are working and that we can pull fields from Dimension Tables and Fact Tables.
13) (24:16) Build PivotTable Report to show Net Revenue for each Country.
14) (25:33) “Trouble Shooting” Part of Example: Tracking Down Error between web site data and company data, including finding error and updating Country Code Excel Table and refreshing the linked table in the Data Model so that the PivotTable report updates and has correct results.
15) (30:35) Hide Fields from Field Lists using “Hide From Client Tools”
16) (31:59) Build PivotTable to see that with a Data Model PivotTable you can NOT group Dates
17) (33:20) Create DAX Calculated Column for Month Number and Month Name. See the DAX functions: MONTH and FORMAT (Like TEXT in Excel).
18) (35:20) Build PivotTable to see that Month Name does NOT sort correctly in a Data Model PivotTable.
19) (35:52) Learn how to Sort Month Name column by Month Number so that Month Name sorts correctly in a data Model PivotTable
20) (36:36) Build relationship between Calendar Table and Fact Table
21) (36:36) Build PivotTable Report to show Net Revenue by Month
22) (37:33) Create DAX Calculated Column for Year. See the DAX function: YEAR.
23) (38:02) Build PivotTable Report to show Net Revenue by Month & Year
24) (39:10) Build DAX Calculated Column for COGS for each transaction. See the DAX functions: RELATED (Like VLOOKUP in Excel) and ROUND.
25) (40:55) Build DAX Measure (Calculated Field) for Total COGS (Overall Total COGS for entire Fact table). See the DAX function: SUM.
26) (41:29) Build DAX Measure (Calculated Field) for Gross Profit using Measures (Calculated Fields) in our DAX formula.
27) (42:21) Build PivotTable Report to show Net Revenue, COGS and Gross Profit for each Year and Month.
28) (44:04) Build PivotTable Report to show Percentage Change for Net Revenue over Same Period Last Year.
29) (45:00) Build DAX Measure (Calculated Field) for Percentage Change over Same Period Last Year using the DAX functions: CALCULATE, SAMEPERIODLASTYEAR and IF.
30) (52:22) Build PivotTable Report to show Percentage of Grand Total for Each Product. Concept behind the formula.
31) (54:11) Build DAX Measure (Calculated Field) for Percentage of Grand Total using the DAX functions: CALCULATE and ALL.
32) (56:45) Refresh Reports when source data changes. In our example we bring 7 million rows into the Excel PowerPivot Data Model.
33) (57:48) Update Calendar table
34) (59:34) Summary and Conclusion.
Download Excel File Not: After clicking on link, Use Ctrl + F (Find) and search for “Highline BI 348 Class” or for the file name as seen at the beginning of the video.
More about CALCULATE function: https://www.youtube.com/watch?v=kMMohkVk8Ds
PowerPivot Playlist: https://www.youtube.com/playlist?list=PLrRPvpgDmw0nGCx21PRFbsJpUIH06LKs-
wn.com/Basic Excel Business Analytics 42 Comprehensive Powerpivot, Data Model, Dax Reporting Example
Download file from “Highline BI 348 Class” section: https://people.highline.edu/mgirvin/excelisfun.htm
Learn about PowerPivot: Import Big Data, Build Data Model, Create Reports:
1) (00:04) Info about files for project
2) (00:20) Intro to Video and look at end result reports
3) (01:56) Overview of steps in building a Data Model in PowerPivot
4) (03:03) Look at source data files including text files with 5 million rows of records
5) (04:46) From Text Files, use Power Query to import 5 million rows of transactional sales data into the a Fact Table in the Data Model in PowerPivot
6) (09:48) From Excel file, use Power Query to import Product Retail and Standard Cost Dimension Table into the Data Model in PowerPivot
7) (10:52) Build Calendar Dimension Table in Excel and then use Power Query to import into the Data Model in PowerPivot
8) (13:44) From web get ISO 3166-1 County Code Data to build and then use Power Query to import Country Code Dimension Table into the Data Model in PowerPivot
9) (17:50) Build One-to-Many Relationships between Fact Table and Dimension Tables
10) (20:16) Build DAX Calculated Column for Net Revenue for each transaction. See the DAX functions: RELATED (Like VLOOKUP in Excel) and ROUND.
11) (22:30) Build DAX Measure (Calculated Field) for Total Net Revenue (Overall Total Net Revenue for entire Fact table). See the DAX function: SUM.
12) (24:16) Build PivotTable to see that the relationships are working and that we can pull fields from Dimension Tables and Fact Tables.
13) (24:16) Build PivotTable Report to show Net Revenue for each Country.
14) (25:33) “Trouble Shooting” Part of Example: Tracking Down Error between web site data and company data, including finding error and updating Country Code Excel Table and refreshing the linked table in the Data Model so that the PivotTable report updates and has correct results.
15) (30:35) Hide Fields from Field Lists using “Hide From Client Tools”
16) (31:59) Build PivotTable to see that with a Data Model PivotTable you can NOT group Dates
17) (33:20) Create DAX Calculated Column for Month Number and Month Name. See the DAX functions: MONTH and FORMAT (Like TEXT in Excel).
18) (35:20) Build PivotTable to see that Month Name does NOT sort correctly in a Data Model PivotTable.
19) (35:52) Learn how to Sort Month Name column by Month Number so that Month Name sorts correctly in a data Model PivotTable
20) (36:36) Build relationship between Calendar Table and Fact Table
21) (36:36) Build PivotTable Report to show Net Revenue by Month
22) (37:33) Create DAX Calculated Column for Year. See the DAX function: YEAR.
23) (38:02) Build PivotTable Report to show Net Revenue by Month & Year
24) (39:10) Build DAX Calculated Column for COGS for each transaction. See the DAX functions: RELATED (Like VLOOKUP in Excel) and ROUND.
25) (40:55) Build DAX Measure (Calculated Field) for Total COGS (Overall Total COGS for entire Fact table). See the DAX function: SUM.
26) (41:29) Build DAX Measure (Calculated Field) for Gross Profit using Measures (Calculated Fields) in our DAX formula.
27) (42:21) Build PivotTable Report to show Net Revenue, COGS and Gross Profit for each Year and Month.
28) (44:04) Build PivotTable Report to show Percentage Change for Net Revenue over Same Period Last Year.
29) (45:00) Build DAX Measure (Calculated Field) for Percentage Change over Same Period Last Year using the DAX functions: CALCULATE, SAMEPERIODLASTYEAR and IF.
30) (52:22) Build PivotTable Report to show Percentage of Grand Total for Each Product. Concept behind the formula.
31) (54:11) Build DAX Measure (Calculated Field) for Percentage of Grand Total using the DAX functions: CALCULATE and ALL.
32) (56:45) Refresh Reports when source data changes. In our example we bring 7 million rows into the Excel PowerPivot Data Model.
33) (57:48) Update Calendar table
34) (59:34) Summary and Conclusion.
Download Excel File Not: After clicking on link, Use Ctrl + F (Find) and search for “Highline BI 348 Class” or for the file name as seen at the beginning of the video.
More about CALCULATE function: https://www.youtube.com/watch?v=kMMohkVk8Ds
PowerPivot Playlist: https://www.youtube.com/playlist?list=PLrRPvpgDmw0nGCx21PRFbsJpUIH06LKs-
- published: 29 Oct 2015
- views: 7343
Learning Scene-Specific Pedestrian Detectors without Real Data, CVPR 2015
- Order: Reorder
- Duration: 2:12
- Updated: 13 Apr 2015
- views: 989
Project Page: http://vishnu.boddeti.net/projects/detection-by-synthesis.html
CVPR 2015 Paper Title:
Learning Scene-Specific Pedestrian Detectors without Real D...
Project Page: http://vishnu.boddeti.net/projects/detection-by-synthesis.html
CVPR 2015 Paper Title:
Learning Scene-Specific Pedestrian Detectors without Real Data
Authors:
Hironori Hattori, Vishnu Naresh Boddeti, Kris Kitani, Takeo Kanade
Abstract:
We consider the problem of designing a scene-specific pedestrian detector in a scenario where we have zero instances of real pedestrian data (i.e., no labeled real data or unsupervised real data). This scenario may arise when a new surveillance system is installed in a novel location and a scene-specific pedestrian detector must be trained prior to any observations of pedestrians. The key idea of our approach is to infer the potential appearance of pedestrians using geometric scene data and a customizable database of virtual simulations of pedestrian motion. We propose an efficient discriminative learning method that generates a spatially-varying pedestrian appearance model that takes into the account the perspective geometry of the scene. As a result, our method is able to learn a unique pedestrian classifier customized for every possible location in the scene. Our experimental results show that our proposed approach outperforms classical pedestrian detection models and hybrid synthetic-real models. Our results also yield a surprising result, that our method using purely synthetic data is able to outperform models trained on real scene-specific data when data is limited.
wn.com/Learning Scene Specific Pedestrian Detectors Without Real Data, Cvpr 2015
Project Page: http://vishnu.boddeti.net/projects/detection-by-synthesis.html
CVPR 2015 Paper Title:
Learning Scene-Specific Pedestrian Detectors without Real Data
Authors:
Hironori Hattori, Vishnu Naresh Boddeti, Kris Kitani, Takeo Kanade
Abstract:
We consider the problem of designing a scene-specific pedestrian detector in a scenario where we have zero instances of real pedestrian data (i.e., no labeled real data or unsupervised real data). This scenario may arise when a new surveillance system is installed in a novel location and a scene-specific pedestrian detector must be trained prior to any observations of pedestrians. The key idea of our approach is to infer the potential appearance of pedestrians using geometric scene data and a customizable database of virtual simulations of pedestrian motion. We propose an efficient discriminative learning method that generates a spatially-varying pedestrian appearance model that takes into the account the perspective geometry of the scene. As a result, our method is able to learn a unique pedestrian classifier customized for every possible location in the scene. Our experimental results show that our proposed approach outperforms classical pedestrian detection models and hybrid synthetic-real models. Our results also yield a surprising result, that our method using purely synthetic data is able to outperform models trained on real scene-specific data when data is limited.
- published: 13 Apr 2015
- views: 989
-
Geometric Madness with Jason Davies
Jason Davies discusses his implementation of various computational geometry algorithms for the Web.Talk was at The Graphical Web 2014 conference (http://www.graphicalweb.org/2014/) at The University of Winchester as organised by The Office for National Statistics. Video by John Wilson (@snoop2003) of Winchester University Journalism School. -
Smartgeometry 2013 -- Constructing Data: Behavioral Complexity
Smartgeometry 2013 -- Day 1 Session 1 -- Constructing Data: Behavioral Complexity Presented by: Theo Spyropoulos - Architectural Association The 2013 Smartgeometry Conference in London, UK focused on the theme of constructing for uncertainty. About Smartgeometry Conference: This widely acclaimed conference is the world's premier event focused on computational and parametric design tools, technologies, and methodologies that allow and encourage new forms of architectural and structural expression. Learn more: http://bit.ly/19PAEw2 About GenerativeComponents: GenerativeComponents (GC) enables designers to explore more possibilities, in less time, create better designs, and efficiently create and manage complex geometric relationships. Learn more: http://bit.ly/17Pev0o About Bentley Sy... -
Investing in Data and A.I. // Dan Scholnick, Trinity Ventures (Hosted by FirstMark Capital)
Dan Scholnick, General Partner at Trinity Ventures, spoke at FirstMark's Data Driven NYC on January 19, 2016. Scholnick shared what excites him about investing in big data and artificial intelligence. Founded in 1986, Trinity Ventures is an early stage venture capital firm. Data Driven NYC is a monthly event covering Big Data and data-driven products and startups, hosted by Matt Turck, partner at FirstMark. FirstMark is an early stage venture capital firm based in New York City. Find out more about Data Driven NYC at http://datadrivennyc.com and FirstMark Capital at http://firstmarkcap.com. -
How to Easily Load Data into ArcGIS for Local Government
Find out how to take advantage of the 50+ maps and apps available with ArcGIS for Local Government. You'll first learn about the benefits of this initiative from Esri's ArcGIS for Local Government Project Manager, Scott Oppmann. Then, via demos from Steve MacCabe (Safe Software) and Steve Grise (Vertex3), you'll discover how to easily load data into this information model and perform QC during the load process to maintain data integrity. -
The Benefits of Fast Business Intelligence // Amir Orad, Sisense (Hosted by FirstMark Capital)
Amir Orad, CEO of Sisense, presented at FirstMark's Data Driven NYC on January 19, 2016. Orad discussed Sisense's approach to speed in business analytics. Sisense creates business analytics software that lets you easily prepare, analyze and visualize complex datasets using one Single-Stack solution. Data Driven NYC is a monthly event covering Big Data and data-driven products and startups, hosted by Matt Turck, partner at FirstMark. FirstMark is an early stage venture capital firm based in New York City. Find out more about Data Driven NYC at http://datadrivennyc.com and FirstMark Capital at http://firstmarkcap.com. -
Virtual Cities Unity3d Tutorial 9
In this tutorial we create a workplace with jobs and the humans start working. Project is available here: http://bit.ly/1SXufjA C# and Unity3d concepts Tools and Tricks: - Geometric mean algorithm in C# - Vector2 - Debugging Unity3d in play mode with Visual Studio
Geometric Madness with Jason Davies
- Order: Reorder
- Duration: 37:02
- Updated: 02 Oct 2014
- views: 3401
Jason Davies discusses his implementation of various computational geometry algorithms for the Web.Talk was at The Graphical Web 2014 conference (http://www.gra...
Jason Davies discusses his implementation of various computational geometry algorithms for the Web.Talk was at The Graphical Web 2014 conference (http://www.graphicalweb.org/2014/) at The University of Winchester as organised by The Office for National Statistics. Video by John Wilson (@snoop2003) of Winchester University Journalism School.
wn.com/Geometric Madness With Jason Davies
Jason Davies discusses his implementation of various computational geometry algorithms for the Web.Talk was at The Graphical Web 2014 conference (http://www.graphicalweb.org/2014/) at The University of Winchester as organised by The Office for National Statistics. Video by John Wilson (@snoop2003) of Winchester University Journalism School.
- published: 02 Oct 2014
- views: 3401
Smartgeometry 2013 -- Constructing Data: Behavioral Complexity
- Order: Reorder
- Duration: 22:04
- Updated: 09 Jul 2013
- views: 407
Smartgeometry 2013 -- Day 1 Session 1 -- Constructing Data: Behavioral Complexity
Presented by: Theo Spyropoulos - Architectural Association
The 2013 Smartgeom...
Smartgeometry 2013 -- Day 1 Session 1 -- Constructing Data: Behavioral Complexity
Presented by: Theo Spyropoulos - Architectural Association
The 2013 Smartgeometry Conference in London, UK focused on the theme of constructing for uncertainty.
About Smartgeometry Conference: This widely acclaimed conference is the world's premier event focused on computational and parametric design tools, technologies, and methodologies that allow and encourage new forms of architectural and structural expression. Learn more: http://bit.ly/19PAEw2
About GenerativeComponents: GenerativeComponents (GC) enables designers to explore more possibilities, in less time, create better designs, and efficiently create and manage complex geometric relationships. Learn more: http://bit.ly/17Pev0o
About Bentley Systems: Bentley is the global leader dedicated to providing architects, engineers, geospatial professionals, constructors, and owner-operators with comprehensive software solutions for sustaining infrastructure. Learn more: http://bit.ly/13m4WDd
wn.com/Smartgeometry 2013 Constructing Data Behavioral Complexity
Smartgeometry 2013 -- Day 1 Session 1 -- Constructing Data: Behavioral Complexity
Presented by: Theo Spyropoulos - Architectural Association
The 2013 Smartgeometry Conference in London, UK focused on the theme of constructing for uncertainty.
About Smartgeometry Conference: This widely acclaimed conference is the world's premier event focused on computational and parametric design tools, technologies, and methodologies that allow and encourage new forms of architectural and structural expression. Learn more: http://bit.ly/19PAEw2
About GenerativeComponents: GenerativeComponents (GC) enables designers to explore more possibilities, in less time, create better designs, and efficiently create and manage complex geometric relationships. Learn more: http://bit.ly/17Pev0o
About Bentley Systems: Bentley is the global leader dedicated to providing architects, engineers, geospatial professionals, constructors, and owner-operators with comprehensive software solutions for sustaining infrastructure. Learn more: http://bit.ly/13m4WDd
- published: 09 Jul 2013
- views: 407
Investing in Data and A.I. // Dan Scholnick, Trinity Ventures (Hosted by FirstMark Capital)
- Order: Reorder
- Duration: 28:50
- Updated: 25 Jan 2016
- views: 238
Dan Scholnick, General Partner at Trinity Ventures, spoke at FirstMark's Data Driven NYC on January 19, 2016. Scholnick shared what excites him about investing ...
Dan Scholnick, General Partner at Trinity Ventures, spoke at FirstMark's Data Driven NYC on January 19, 2016. Scholnick shared what excites him about investing in big data and artificial intelligence.
Founded in 1986, Trinity Ventures is an early stage venture capital firm.
Data Driven NYC is a monthly event covering Big Data and data-driven products and startups, hosted by Matt Turck, partner at FirstMark.
FirstMark is an early stage venture capital firm based in New York City. Find out more about Data Driven NYC at http://datadrivennyc.com and FirstMark Capital at http://firstmarkcap.com.
wn.com/Investing In Data And A.I. Dan Scholnick, Trinity Ventures (Hosted By Firstmark Capital)
Dan Scholnick, General Partner at Trinity Ventures, spoke at FirstMark's Data Driven NYC on January 19, 2016. Scholnick shared what excites him about investing in big data and artificial intelligence.
Founded in 1986, Trinity Ventures is an early stage venture capital firm.
Data Driven NYC is a monthly event covering Big Data and data-driven products and startups, hosted by Matt Turck, partner at FirstMark.
FirstMark is an early stage venture capital firm based in New York City. Find out more about Data Driven NYC at http://datadrivennyc.com and FirstMark Capital at http://firstmarkcap.com.
- published: 25 Jan 2016
- views: 238
How to Easily Load Data into ArcGIS for Local Government
- Order: Reorder
- Duration: 56:34
- Updated: 30 Aug 2012
- views: 1942
Find out how to take advantage of the 50+ maps and apps available with ArcGIS for Local Government. You'll first learn about the benefits of this initiative fro...
Find out how to take advantage of the 50+ maps and apps available with ArcGIS for Local Government. You'll first learn about the benefits of this initiative from Esri's ArcGIS for Local Government Project Manager, Scott Oppmann. Then, via demos from Steve MacCabe (Safe Software) and Steve Grise (Vertex3), you'll discover how to easily load data into this information model and perform QC during the load process to maintain data integrity.
wn.com/How To Easily Load Data Into Arcgis For Local Government
Find out how to take advantage of the 50+ maps and apps available with ArcGIS for Local Government. You'll first learn about the benefits of this initiative from Esri's ArcGIS for Local Government Project Manager, Scott Oppmann. Then, via demos from Steve MacCabe (Safe Software) and Steve Grise (Vertex3), you'll discover how to easily load data into this information model and perform QC during the load process to maintain data integrity.
- published: 30 Aug 2012
- views: 1942
The Benefits of Fast Business Intelligence // Amir Orad, Sisense (Hosted by FirstMark Capital)
- Order: Reorder
- Duration: 23:00
- Updated: 25 Jan 2016
- views: 556
Amir Orad, CEO of Sisense, presented at FirstMark's Data Driven NYC on January 19, 2016. Orad discussed Sisense's approach to speed in business analytics.
Sise...
Amir Orad, CEO of Sisense, presented at FirstMark's Data Driven NYC on January 19, 2016. Orad discussed Sisense's approach to speed in business analytics.
Sisense creates business analytics software that lets you easily prepare, analyze and visualize complex datasets using one Single-Stack solution.
Data Driven NYC is a monthly event covering Big Data and data-driven products and startups, hosted by Matt Turck, partner at FirstMark.
FirstMark is an early stage venture capital firm based in New York City. Find out more about Data Driven NYC at http://datadrivennyc.com and FirstMark Capital at http://firstmarkcap.com.
wn.com/The Benefits Of Fast Business Intelligence Amir Orad, Sisense (Hosted By Firstmark Capital)
Amir Orad, CEO of Sisense, presented at FirstMark's Data Driven NYC on January 19, 2016. Orad discussed Sisense's approach to speed in business analytics.
Sisense creates business analytics software that lets you easily prepare, analyze and visualize complex datasets using one Single-Stack solution.
Data Driven NYC is a monthly event covering Big Data and data-driven products and startups, hosted by Matt Turck, partner at FirstMark.
FirstMark is an early stage venture capital firm based in New York City. Find out more about Data Driven NYC at http://datadrivennyc.com and FirstMark Capital at http://firstmarkcap.com.
- published: 25 Jan 2016
- views: 556
Virtual Cities Unity3d Tutorial 9
- Order: Reorder
- Duration: 58:52
- Updated: 04 Apr 2016
- views: 37
In this tutorial we create a workplace with jobs and the humans start working.
Project is available here:
http://bit.ly/1SXufjA
C# and Unity3d concepts Tools ...
In this tutorial we create a workplace with jobs and the humans start working.
Project is available here:
http://bit.ly/1SXufjA
C# and Unity3d concepts Tools and Tricks:
- Geometric mean algorithm in C#
- Vector2
- Debugging Unity3d in play mode with Visual Studio
wn.com/Virtual Cities Unity3D Tutorial 9
In this tutorial we create a workplace with jobs and the humans start working.
Project is available here:
http://bit.ly/1SXufjA
C# and Unity3d concepts Tools and Tricks:
- Geometric mean algorithm in C#
- Vector2
- Debugging Unity3d in play mode with Visual Studio
- published: 04 Apr 2016
- views: 37
close fullscreen
- Playlist
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close fullscreen
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22:31
Can A.I. Become More Human? // Gary Marcus, Geometric Intelligence (Hosted by FirstMark Capital)
Gary Marcus, CEO at Geometric Intelligence, presented at FirstMark's Data Driven NYC on Ja...
published: 25 Jan 2016
Can A.I. Become More Human? // Gary Marcus, Geometric Intelligence (Hosted by FirstMark Capital)
Can A.I. Become More Human? // Gary Marcus, Geometric Intelligence (Hosted by FirstMark Capital)
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- published: 25 Jan 2016
- views: 2152
6:48
X68K MDX FM音源64音（OPM*8個）+ PCM12音 RAYSTORM GEOMETRIC CITY+おまけ（1080p）
フルHD動画です。
※ヘッドフォン推奨。
FM64音+PCM12音使って本気で作ってみました。
後半はオマケ曲です。
8音ずつのMDXファイルを8つ作って同期再生させる事でFM6...
published: 23 Oct 2012
X68K MDX FM音源64音（OPM*8個）+ PCM12音 RAYSTORM GEOMETRIC CITY+おまけ（1080p）
X68K MDX FM音源64音（OPM*8個）+ PCM12音 RAYSTORM GEOMETRIC CITY+おまけ（1080p）
- Report rights infringement
- published: 23 Oct 2012
- views: 16814
12:50
David Moloney, CTO Movidius. Drones, Data X Conference 2015
Movidius CTO David Moloney talks about the future of pilot-less drones thanks to machine v...
published: 19 Nov 2015
David Moloney, CTO Movidius. Drones, Data X Conference 2015
David Moloney, CTO Movidius. Drones, Data X Conference 2015
- Report rights infringement
- published: 19 Nov 2015
- views: 573
1:19
The Data Center Mural Project: Painting a Cloud
The St. Ghislain, Belgium data center wall goes from a blank canvas to a colorful mural by...
published: 12 May 2016
The Data Center Mural Project: Painting a Cloud
The Data Center Mural Project: Painting a Cloud
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- published: 12 May 2016
- views: 2219
79:36
3. Geometric Structures I
MIT 6.851 Advanced Data Structures, Spring 2012
View the complete course: http://ocw.mit.e...
published: 18 Dec 2013
3. Geometric Structures I
3. Geometric Structures I
- Report rights infringement
- published: 18 Dec 2013
- views: 7855
61:15
Data Mining (Advanced Analytics): Sparse Learning & Dimension Reduction
For tips on how to plan successful data mining projects, read this IBM white paper: http:/...
published: 23 Jan 2014
Data Mining (Advanced Analytics): Sparse Learning & Dimension Reduction
Data Mining (Advanced Analytics): Sparse Learning & Dimension Reduction
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- published: 23 Jan 2014
- views: 3056
1:01
ArcMap 10: Calculating Area by Boundary using Calculate Geometry (basic map calculation)
It's all about calculating the area for a defined boundary. One advantage with GIS is the...
published: 22 Apr 2013
ArcMap 10: Calculating Area by Boundary using Calculate Geometry (basic map calculation)
ArcMap 10: Calculating Area by Boundary using Calculate Geometry (basic map calculation)
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- published: 22 Apr 2013
- views: 1662
4:25
ArcMap 10: How to delineate a watershed using a DEM: Calculate geometry (4 of 4)
Thanks for joining. Sorry I didn't download a stream dataset. This video will only focus...
published: 21 May 2013
ArcMap 10: How to delineate a watershed using a DEM: Calculate geometry (4 of 4)
ArcMap 10: How to delineate a watershed using a DEM: Calculate geometry (4 of 4)
- Report rights infringement
- published: 21 May 2013
- views: 8019
20:47
Smartgeometry 2013 -- Constructing Data: Realizing Algorithm
Smartgeometry 2013 -- Day 1 Session 1 -- Constructing Data: Realizing Algorithm Presented ...
published: 09 Jul 2013
Smartgeometry 2013 -- Constructing Data: Realizing Algorithm
Smartgeometry 2013 -- Constructing Data: Realizing Algorithm
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- published: 09 Jul 2013
- views: 926
43:07
Caching is Your Friend - Creating Tons of Maps on the Fly and Auto-Updating Some
By Julie Goldberg
Julie Goldberg is the co-founder and chief engineer behind Empower Engi...
published: 16 Mar 2015
Caching is Your Friend - Creating Tons of Maps on the Fly and Auto-Updating Some
Caching is Your Friend - Creating Tons of Maps on the Fly and Auto-Updating Some
- Report rights infringement
- published: 16 Mar 2015
- views: 300
61:05
Basic Excel Business Analytics #42: Comprehensive PowerPivot, Data Model, DAX & Reporting Example
Download file from “Highline BI 348 Class” section: https://people.highline.edu/mgirvin/ex...
published: 29 Oct 2015
Basic Excel Business Analytics #42: Comprehensive PowerPivot, Data Model, DAX & Reporting Example
Basic Excel Business Analytics #42: Comprehensive PowerPivot, Data Model, DAX & Reporting Example
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- published: 29 Oct 2015
- views: 7343
2:12
Learning Scene-Specific Pedestrian Detectors without Real Data, CVPR 2015
Project Page: http://vishnu.boddeti.net/projects/detection-by-synthesis.html
CVPR 2015 Pa...
published: 13 Apr 2015
Learning Scene-Specific Pedestrian Detectors without Real Data, CVPR 2015
Learning Scene-Specific Pedestrian Detectors without Real Data, CVPR 2015
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- published: 13 Apr 2015
- views: 989
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37:02
Geometric Madness with Jason Davies
Jason Davies discusses his implementation of various computational geometry algorithms for...
published: 02 Oct 2014
Geometric Madness with Jason Davies
Geometric Madness with Jason Davies
- Report rights infringement
- published: 02 Oct 2014
- views: 3401
22:04
Smartgeometry 2013 -- Constructing Data: Behavioral Complexity
Smartgeometry 2013 -- Day 1 Session 1 -- Constructing Data: Behavioral Complexity
Presente...
published: 09 Jul 2013
Smartgeometry 2013 -- Constructing Data: Behavioral Complexity
Smartgeometry 2013 -- Constructing Data: Behavioral Complexity
- Report rights infringement
- published: 09 Jul 2013
- views: 407
28:50
Investing in Data and A.I. // Dan Scholnick, Trinity Ventures (Hosted by FirstMark Capital)
Dan Scholnick, General Partner at Trinity Ventures, spoke at FirstMark's Data Driven NYC o...
published: 25 Jan 2016
Investing in Data and A.I. // Dan Scholnick, Trinity Ventures (Hosted by FirstMark Capital)
Investing in Data and A.I. // Dan Scholnick, Trinity Ventures (Hosted by FirstMark Capital)
- Report rights infringement
- published: 25 Jan 2016
- views: 238
56:34
How to Easily Load Data into ArcGIS for Local Government
Find out how to take advantage of the 50+ maps and apps available with ArcGIS for Local Go...
published: 30 Aug 2012
How to Easily Load Data into ArcGIS for Local Government
How to Easily Load Data into ArcGIS for Local Government
- Report rights infringement
- published: 30 Aug 2012
- views: 1942
23:00
The Benefits of Fast Business Intelligence // Amir Orad, Sisense (Hosted by FirstMark Capital)
Amir Orad, CEO of Sisense, presented at FirstMark's Data Driven NYC on January 19, 2016. O...
published: 25 Jan 2016
The Benefits of Fast Business Intelligence // Amir Orad, Sisense (Hosted by FirstMark Capital)
The Benefits of Fast Business Intelligence // Amir Orad, Sisense (Hosted by FirstMark Capital)
- Report rights infringement
- published: 25 Jan 2016
- views: 556
58:52
Virtual Cities Unity3d Tutorial 9
In this tutorial we create a workplace with jobs and the humans start working.
Project is...
published: 04 Apr 2016
Virtual Cities Unity3d Tutorial 9
Virtual Cities Unity3d Tutorial 9
- Report rights infringement
- published: 04 Apr 2016
- views: 37
Geometric Madness with Jason Davies...
Smartgeometry 2013 -- Constructing Data: Behaviora...
Investing in Data and A.I. // Dan Scholnick, Trin...
How to Easily Load Data into ArcGIS for Local Gove...
The Benefits of Fast Business Intelligence // Amir...
Virtual Cities Unity3d Tutorial 9...
photo: Creative Commons / Ted Eytan
Dems show true colors, want Clinton to run even if indicted
Edit The Examiner 31 May 2016
In a stunning revelation, 71 percent of likely voters who identified themselves as Democrats told the Rasmussen survey, published today, that they believe Hillary Rodham Clinton should continue running for the presidency if she is indicted in connection with the use of a private e-mail server while Secretary of State. Today’s Seattle Times fact-checks some of her statements regarding the e-mail scandal ... ....
photo: AP / U.S. Customs and Border Protection via AP
U.S. Government Seeks Seizure Of Insurance Payments To San Bernardino Shooter's Family
Edit WorldNews.com 01 Jun 2016
U.S. Atty ... “We will continue to use every tool available to seek justice on behalf of the victims of the San Bernardino terrorist attacks.”Farook and his wife, Tashfeen Malik, opened fire on San Bernardino County workers at a holiday party on Dec ... ....
photo: UN / Mark Garten
Russian Foreign Minister: 'Nyet' On Ceding Kurils To Japan
Edit WorldNews.com 31 May 2016
Russian Foreign Minister Sergei Lavrov Tuesday announced his country has no plans to cede parts of the contested Kuril Islands to Japan, a China Daily report said. "We aren't doing this and we won't do this ... He also said the sanctions against Russia imposed by the United States and its allies should be seen as an opportunity to diversify the Russian economy.– WN.com, Jack Durschlag....
photo: AP / Randy Holmes/ABC via AP
Debate That Should Have Been Shows Who’s Committed To Democracy And Who’s Committed To Backing Out
Edit WorldNews.com 31 May 2016
Article by WN.com Correspondent Dallas DarlingMany Americans are still disappointed over the debate that should have been between Donald Trump and Bernie Sanders. Indeed, feathers flew over the weekend as some called the presumptive Republican presidential candidate a “chicken” and “coward” for making excuses to back out ... Sanders, for his part, immediately responded with “Game On.” ... Hispanic Chamber of Commerce (USHCC) ... more democratic ... ....
photo: Creative Commons / Quadell
Backlash Over Killing Of Gorilla Scapegoats Mother Of Tot Who Fell Into Zoo Enclosure
Edit Inquisitr 31 May 2016
Backlash over the killing of a gorilla at the Cincinnati Zoo has fanned social media anger against the mother of the tot who slipped into a restricted area where he was in danger of being mauled. At the same time, witnesses of the incident are trying to put things in perspective and calm passions against the mother’s “negligence” or the zoo’s decision that led to the shooting of a rare gorilla....
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Identify two most polluted cities in each state, study emissions from diesel, petrol: NGT to states
Edit DNA India 01 Jun 2016
Additionally, it has asked states to file a detailed affidavit on vehicular pollution in these two cities, the different sources of pollution, data on petrol and diesel emissions, ambient air quality and impact of mining and industries on air quality ... Earlier on Monday, the NGT bench had asked all states to submit data on cities worst affected by pollution....
'Visitor safety key at city beach'
Edit The Times of India 01 Jun 2016
including city police, Greater Visakhapatnam Municipal Corporation, Visakhapatnam Urban Development Authority (VUDA), among others at the collectorate here on Tuesday, the collector said that they ......
Wilson Therapeutics Presents Encouraging Clinical Data On Decuprate At The Congress Of The
Edit Topix 01 Jun 2016
Regulatory News. Wilson Therapeutics AB , announces that preliminary data from the company's ongoing Phase II clinical trial for DecuprateA in Wilson Disease were presented at the 2ndCongress of the European Academy of Neurology, taking place in Copenhagen, Denmark, May 28-31. The oral presentation of the data was given by Prof ... ....
Ionic Security to Deliver High-Assurance Global Data Protection & Control Services in Minutes for Regulated ...
Edit Business Wire 01 Jun 2016
ATLANTA--(BUSINESS WIRE)--Ionic Security Inc., a pioneer of high-assurance data protection and control, announced today that it has collaborated with Amazon Web Services (AWS) to deliver its hybrid high-assurance global data protection and control services via AWS Marketplace on the industry’s most secure infrastructure platform ... ....
City Maintains High Credit Ratings 2016 (City of Tucson, AZ)
Edit Public Technologies 01 Jun 2016
City of Tucson, AZ) Contact ... lane.mandle@tucsonaz.gov CITY MAINTAINS CREDIT RATING ... Citing Caterpillars impending move to Tucson, Moody's draft report states, "The city has added approximately 10,000 jobs over the past year, and unemployment has reached a post-recession low of 5%, according to recent data from the Bureau of Labor Statistics." ... The below chart lists the City's credit rating by agency.....
Redfield Energy, LLC: Plant Operator (City of Redfield, SD)
Edit Public Technologies 01 Jun 2016
City of Redfield, SD) ... Individual must learn and operate the DCS computer control system, perform quality control tests on product streams, analyze compiled data and make adjustments to optimize plant production ... City of Redfield, SD published this content on 31 ......
Korea Yields Plunge as Poor Trade Data Intensifies Rate-Cut Odds
Edit Bloomberg 01 Jun 2016
South Korea’s three-year government bond yield fell by the most this year after disappointing trade data added to speculation the central bank will lower interest rates for the first time in 12 months ... Data Wednesday showed exports contracted for a 17th straight month, contributing to the smallest current-account surplus since June 2014 ... in Seoul, data compiled by Bloomberg show....
photo: WN / Yolanda Leyba
Court says police don't need warrants for phone location data
Edit Engadget 01 Jun 2016
You would think that police would require a warrant to get your phone's location info, right? Not according to the US' Fourth Circuit Court of Appeals. It just ruled that asking a company for cellphone location data you've offered to a third-party... ....
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