Deep learning

Deep learning (deep structured learning, hierarchical learning or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using multiple processing layers with complex structures, or otherwise composed of multiple non-linear transformations.

Deep learning is part of a broader family of machine learning methods based on learning representations of data. An observation (e.g., an image) can be represented in many ways such as a vector of intensity values per pixel, or in a more abstract way as a set of edges, regions of particular shape, etc. Some representations make it easier to learn tasks (e.g., face recognition or facial expression recognition) from examples. One of the promises of deep learning is replacing handcrafted features with efficient algorithms for unsupervised or semi-supervised feature learning and hierarchical feature extraction.

Research in this area attempts to make better representations and create models to learn these representations from large-scale unlabeled data. Some of the representations are inspired by advances in neuroscience and are loosely based on interpretation of information processing and communication patterns in a nervous system, such as neural coding which attempts to define a relationship between various stimuli and associated neuronal responses in the brain.

Neural network (disambiguation)

Neural network(s) may refer to:

Artificial neural network, neural network models and devices employed in statistics, cognitive psychology and artificial intelligence

Biological neural network, collections of physically interconnected neurons whose inputs define a recognizable circuit such as the brain and the eyes through the visual cortex

Neural Networks (journal)

Machine learning

Machine learning is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. In 1959, Arthur Samuel defined machine learning as a "Field of study that gives computers the ability to learn without being explicitly programmed". Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. Such algorithms operate by building a model from example inputs in order to make data-driven predictions or decisions, rather than following strictly static program instructions.

Machine learning is closely related to and often overlaps with computational statistics; a discipline which also focuses in prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms is infeasible. Example applications include spam filtering, optical character recognition (OCR),search engines and computer vision. Machine learning is sometimes conflated with data mining, where the latter sub-field focuses more on exploratory data analysis and is known as unsupervised learning.

Learning

Learning is the act of acquiring new, or modifying and reinforcing, existing knowledge, behaviors, skills, values, or preferences and may involve synthesizing different types of information. The ability to learn is possessed by humans, animals, plants and some machines. Progress over time tends to follow a learning curve. It does not happen all at once, but builds upon and is shaped by previous knowledge. To that end, learning may be viewed as a process, rather than a collection of factual and procedural knowledge. Learning produces changes in the organism and the changes produced are relatively permanent.

Human learning may occur as part of education, personal development, schooling, or training. It may be goal-oriented and may be aided by motivation. The study of how learning occurs is part of educational psychology, neuropsychology, learning theory, and pedagogy. Learning may occur as a result of habituation or classical conditioning, seen in many animal species, or as a result of more complex activities such as play, seen only in relatively intelligent animals. Learning may occur consciously or without conscious awareness. Learning that an aversive event can't be avoided nor escaped is called learned helplessness. There is evidence for human behavioral learning prenatally, in which habituation has been observed as early as 32 weeks into gestation, indicating that the central nervous system is sufficiently developed and primed for learning and memory to occur very early on in development.

Deep

Deep or The Deep may refer to:

Places

Deep Lake (disambiguation)

Deep River (Iowa), a minor tributary of the English River

Deep River (North Carolina)

Deep River (Washington), a minor tributary of the Columbia River in the state of Washington

Deep River (Western Australia)

Deep Creek (California), a tributary of the Mojave River

Deep Creek (Florida)

Deep Creek (Pennsylvania), a tributary of Pine Creek

Deep Creek (Texas), a tributary of the Colorado River

Deep Creek (Washington), a tributary of the Spokane River

Deep Creek (Melbourne, Victoria), Australia, tributary of the Maribyrnong River

Deep Voll Brook, New Jersey, also known as Deep Brook

Deep Bay (disambiguation)

Deep Cove, North Vancouver, Canada

Deep Cove, New Zealand

Film and television

The Deep (Animated series). An Australian/Canadian animated series based on the graphic novels of Tom Taylor and James Brouwer.

The Deep (unfinished film), a 1970 unfinished film directed by Orson Welles

The Deep (1977 film), based on the novel by Peter Benchley, starring Robert Shaw, Jacqueline Bisset and Nick Nolte

30:54

Irene Chen A Beginner's Guide to Deep Learning PyCon 2016

Irene Chen A Beginner's Guide to Deep Learning PyCon 2016

Irene Chen A Beginner's Guide to Deep Learning PyCon 2016
"Speaker: Irene Chen What is deep learning? It has recently exploded in popularity as a complex and incredibly powerful tool. This talk will present the basic concepts underlying deep learning in understandable pieces for complete beginners to machine learning. We will review the math, code up a simple neural network, and provide contextual background on how deep learning is used in production now. Slides can be found at: https://speakerdeck.com/pycon2016 and https://github.com/PyCon/2016-slides"
22:33

Deep Learning with Neural Networks and TensorFlow Introduction

Deep Learning with Neural Networks and TensorFlow Introduction

Deep Learning with Neural Networks and TensorFlow Introduction
Welcome to a new section in our Machine Learning Tutorial series: Deep Learning with Neural Networks and TensorFlow. The artificial neural network is a biologically-inspired methodology to conduct machine learning, intended to mimic your brain (a biological neural network). The Artificial Neural Network, which I will now just refer to as a neural network, is not a new concept. The idea has been around since the 1940's, and has had a few ups and downs, most notably when compared against the Support Vector Machine (SVM). For example, the Neural Network was popularized up until the mid 90s when it was shown that the SVM, using a new-to-the-public (the technique itself was thought up long before it was actually put to use) technique, the "Kernel Trick," was capable of working with non-linearly separable datasets. With this, the Support Vector Machine catapulted to the front again, leaving neural nets behind and mostly nothing interesting until about 2011, where Deep Neural Networks began to take hold and outperform the Support Vector Machine, using new techniques, huge dataset availability, and much more powerful computers. https://pythonprogramming.net https://twitter.com/sentdex https://www.facebook.com/pythonprogramming.net/ https://plus.google.com/+sentdex
3:47

Deep Learning SIMPLIFIED: The Series Intro - Ep. 1

Deep Learning SIMPLIFIED: The Series Intro - Ep. 1

Deep Learning SIMPLIFIED: The Series Intro - Ep. 1
Are you overwhelmed by overly-technical explanations of Deep Learning? If so, this series will bring you up to speed on this fast-growing field – without any of the math or code. Deep Learning is an important subfield of Artificial Intelligence (AI) that connects various topics like Machine Learning, Neural Networks, and Classification. The field has advanced significantly over the years due to the works of giants like Andrew Ng, Geoff Hinton, Yann LeCun, Adam Gibson, and Andrej Karpathy. Many companies have also invested heavily in Deep Learning and AI research - Google with DeepMind and its Driverless car, nVidia with CUDA and GPU computing, and recently Toyota with its new plan to allocate one billion dollars to AI research. Deep Learning TV on Facebook: https://www.facebook.com/DeepLearningTV/ Twitter: https://twitter.com/deeplearningtv You've probably looked up videos on YouTube and found that most of them contain too much math for a beginner. The few videos that promise to just present concepts are usually still too high level for someone getting started. Any videos that show complicated code just make these problems worse for the viewers. There’s nothing wrong with technical explanations, and to go far in this field you must understand them at some point. However, Deep Learning is a complex topic with a lot of information, so it can be difficult to know where to begin and what path to follow. Does this resonate with you? What are your thoughts? Please comment. The goal of this series is to give you a road map with enough detail that you’ll understand the important concepts, but not so much detail that you’ll feel overwhelmed. The hope is to further explain the concepts that you already know and bring to light the concepts that you need to know. In the end, you’ll be able to decide whether or not to invest additional time on this topic. So while the math and the code are important, you will see neither in this series. The focus is on the intuition behind Deep Learning – what it is, how to use it, who’s behind it, and why it’s important. You'll first get an overview of Deep Learning and a brief introduction of how to choose between different models. Then we'll see some use cases. After that, we’ll discuss various Deep Learning tools including important software libraries and platforms where you can build your own Deep Nets. Some resources: Andrew Ng's Machine learning class - https://www.coursera.org/learn/machine-learning Michael Nielsen's book: http://neuralnetworksanddeeplearning.com/index.html Credits: Nickey Pickorita (YouTube art) - https://www.upwork.com/freelancers/~0147b8991909b20fca Isabel Descutner (Voice) - https://www.youtube.com/user/IsabelDescutner Dan Partynski (Copy Editing) - https://www.linkedin.com/in/danielpartynski Marek Scibior (Prezi creator, Illustrator) - http://brawuroweprezentacje.pl/ Jagannath Rajagopal (Creator, Producer and Director) - https://ca.linkedin.com/in/jagannathrajagopal
3:46

How To Get Started With Machine Learning? | Two Minute Papers #51

How To Get Started With Machine Learning? | Two Minute Papers #51

How To Get Started With Machine Learning? | Two Minute Papers #51
I get a lot of messages from you Fellow Scholars that you would like to get started in machine learning and are looking for materials. Below you find a ton of resources to get you started! __________________________ The AI Revolution: The Road to Superintelligence on Wait But Why: http://waitbutwhy.com/2015/01/artificial-intelligence-revolution-1.html http://waitbutwhy.com/2015/01/artificial-intelligence-revolution-2.html Superintelligence by Nick Bostrom: https://en.wikipedia.org/wiki/Superintelligence:_Paths,_Dangers,_Strategies Courses: Welch Labs - https://www.youtube.com/playlist?list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU Andrew Ng on Coursera - https://class.coursera.org/ml-005/lecture Andrew Ng (YouTube playlist) - https://www.youtube.com/playlist?list=PLA89DCFA6ADACE599 Nando de Freitas (UBC) - https://www.youtube.com/playlist?list=PLE6Wd9FR--Ecf_5nCbnSQMHqORpiChfJf Nando de Freitas (Oxford) - https://www.youtube.com/playlist?list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu Nando de Freitas (more) - https://www.youtube.com/playlist?list=PLE6Wd9FR--EdyJ5lbFl8UuGjecvVw66F6 https://www.youtube.com/watch?v=PlhFWT7vAEw&list;=PLjK8ddCbDMphIMSXn-w1IjyYpHU3DaUYw One more at Caltech - https://work.caltech.edu/telecourse.html Andrej Karpathy - https://www.youtube.com/playlist?list=PLkt2uSq6rBVctENoVBg1TpCC7OQi31AlC UC Berkeley - https://www.youtube.com/channel/UCshmLD2MsyqAKBx8ctivb5Q/videos Geoffrey Hinton - https://www.coursera.org/course/neuralnets Machine Learning specialization at Coursera - https://www.coursera.org/specializations/machine-learning MIT - http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/lecture-videos/ "Pattern Recognition and Machine Learning" by Christoper Bishop: http://research.microsoft.com/en-us/um/people/cmbishop/prml/ "Algorithms for Reinforcement Learning" by Csaba Szepesvári: http://www.ualberta.ca/~szepesva/papers/RLAlgsInMDPs.pdf A great talk on deep learning libraries: https://www.youtube.com/watch?v=Vf_-OkqbwPo&feature;=youtu.be Two great sources to check for new papers: http://gitxiv.com/top http://www.arxiv-sanity.com/top Recent machine learning papers on the arXiv: http://arxiv.org/list/stat.ML/recent The Machine Learning Reddit: http://www.reddit.com/r/MachineLearning/ One more great post on how to get started with machine learning: https://www.quora.com/How-do-I-get-started-in-machine-learning-both-theory-and-programming/answer/Sebastian-Raschka-1 A great blog post on how to get started with Keras: http://swanintelligence.com/first-steps-with-neural-nets-in-keras.html A website with lots of intuitive articles on deep learning: http://neuralnetworksanddeeplearning.com/ A free book on deep learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville: http://www.deeplearningbook.org/ WE'D LIKE TO THANK OUR GENEROUS SUPPORTERS WHO MAKE TWO MINUTE PAPERS POSSIBLE: Sunil Kim, Vinay S. Subscribe if you would like to see more of these! - http://www.youtube.com/subscription_center?add_user=keeroyz The thumbnail background image was created by C_osett - https://flic.kr/p/sDTYmm Splash screen/thumbnail design: Felícia Fehér - http://felicia.hu Károly Zsolnai-Fehér's links: Patreon → https://www.patreon.com/TwoMinutePapers Facebook → https://www.facebook.com/TwoMinutePapers/ Twitter → https://twitter.com/karoly_zsolnai Web → https://cg.tuwien.ac.at/~zsolnai/
33:20

A friendly introduction to Deep Learning and Neural Networks

A friendly introduction to Deep Learning and Neural Networks

A friendly introduction to Deep Learning and Neural Networks
A friendly introduction to neural networks and deep learning. This is a follow up to the Introduction to Machine Learning video. https://www.youtube.com/edit?video_id=IpGxLWOIZy4 Note: In this tutorial I use natural logarithms. If you used logarithms base 10, you may get different answers that I got, although at the end it doesn't matter, since using a different base for the logarithm just scales all the logarithms by a constant.
8:53

How to Learn from Little Data - Intro to Deep Learning #17

How to Learn from Little Data - Intro to Deep Learning #17

How to Learn from Little Data - Intro to Deep Learning #17
One-shot learning! In this last weekly video of the course, i'll explain how memory augmented neural networks can help achieve one-shot classification for a small labeled image dataset. We'll also go over the architecture of it's inspiration (the neural turing machine). Code for this video (with challenge): https://github.com/llSourcell/How-to-Learn-from-Little-Data Please subscribe! And like. And comment. That's what keeps me going. More learning resources: https://www.youtube.com/watch?v=CzQSQ_0Z-QU https://arxiv.org/abs/1605.06065 https://futuristech.info/posts/differential-neural-computer-from-deepmind-and-more-advances-in-backward-propagation https://thenewstack.io/googles-deepmind-ai-now-capable-deep-neural-reasoning/ Join us in the Wizards Slack Channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693
7:59

How to setup a Machine Learning and Deep Learning Environment

How to setup a Machine Learning and Deep Learning Environment

How to setup a Machine Learning and Deep Learning Environment
There are so many tools and choices out there that setting up an environment for newcomers could be daunting and confusing. Here I provide a simple yet powerful setup so you can get started on your ML/DL experimentation
3:27

Build a Neural Net in 4 Minutes

Build a Neural Net in 4 Minutes

Build a Neural Net in 4 Minutes
code for this: https://github.com/stmorgan/pythonNNexample I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ Please Subscribe! That is the thing you could do that would make me happiest. I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty=h&u;=3191693 2 Great Neural Net Tutorials: (please subscribe for more videos like these! ) 1. https://medium.com/technology-invention-and-more/how-to-build-a-simple-neural-network-in-9-lines-of-python-code-cc8f23647ca1#.l51z38s7f 2. https://iamtrask.github.io/2015/07/12/basic-python-network/ Awesome Tutorial Series on Neural Networks: http://lumiverse.io/series/neural-networks-demystified The Canonical Machine Learning Course: https://www.coursera.org/learn/machine-learning Curious just how inspired neural networks are from brain architecture? Take some time to learn about the human brain! This is my favorite intro to neuroscience course: https://www.mcb80x.org/
6:13

Basic Principles of Deep Learning and Neural Networks

Basic Principles of Deep Learning and Neural Networks

Basic Principles of Deep Learning and Neural Networks
In this week's Whiteboard Wednesdays video, Chris Rowen discusses the basic principles of deep learning and how it enables the building of electronic systems that analyze massive amounts of data, recognize patterns, and extract relevant information from speech, images, and social network traffic. Learn more: http://ip.cadence.com/applications/cnn&CMP=IPapp_Vid_YT_CNN_0916 Stay connected! Home: http://goo.gl/tb5eXH Blog: https://goo.gl/3BwM6Y Twitter: http://bit.ly/2ccamZS Facebook: http://bit.ly/2cLrnfV LinkedIn: http://bit.ly/2crspgz
6:09

Turn your laptop into a Deep Learning BEAST

Turn your laptop into a Deep Learning BEAST

Turn your laptop into a Deep Learning BEAST
Setup tutorial of an External Video adapter for Deep Learning. I highly recommend using an Nvidia graphic card, since AMD lacks the CUDA API that most Deep Learning frameworks use. ===Steps=== 1. Get the components: + [Mini PCI-E Version] EXP GDC Laptop External Independent Video Card Dock + [Nvidia] Graphic Card + [at least 400W] Power Supply + Monitor + WiFi dongle 2. Slip your graphics card into the PCIe slot on the BPlus board. 3. Hook your (not yet powered-on) PSU’s 24-pin ATX power supply pins into the BPlus board. 4. Connect the 8-pin PCIe connector on the board to the 6-pin power connector on the graphics card. 5. Insert the PCIe cable into the laptop, then slide the opposite side of the cable (the one with the HDMI connection) into the HDMI port labled “X1” on the PCIe adapter. At this point you'd also connect your graphics card directly to your external monitor, typically via HDMI or DVI. 6. Fire up that beast yo. 7. Disable the built-in graphics and download the drivers for your card. 8. Profit.
57:12

Introduction to Deep Learning part 1

Introduction to Deep Learning part 1

Introduction to Deep Learning part 1
Dr. Jonathan Laserson is a senior algorithm researcher at PointGrab and a Machine Learning expert and consultant. He has a PhD from the Computer Science AI lab at Stanford University and was a lecturer at Bar-Ilan University. After 3 years doing Machine Learning at Google today he is focused on Deep Learning algorithms and their practical usage on embedded architectures. You can read his Hebrew blog about Machine Learning in https://lifesimulator.wordpress.com/. The talk was intended for an audience of engineers at The Generalist Engineer meetup: http://www.meetup.com/The-Generalist-Engineer/ sponsored by Twiggle.com. Link to part 2: https://www.youtube.com/watch?v=E71SNUqi2cw References: The video in the lecture is from Hubel and Wiesel Cat Experiment: https://www.youtube.com/watch?v=IOHayh06LJ4 Used some figures from Andrej Karpathy's Stanford class lecture notes: http://cs231n.github.io/neural-networks-1/ Used some of Ilya Sustkever's slides: http://www.iro.umontreal.ca/~bengioy/cifar/NCAP2014-summerschool/slides/Ilya_LSTMs_for_Translation.pdf Used some of Andrew Ng's slides: http://helper.ipam.ucla.edu/publications/gss2012/gss2012_10595.pdf
1:17:46

Stanford Seminar - "Deep Learning for Dummies" Carey Nachenberg of Symantec and UCLA CS

Stanford Seminar - "Deep Learning for Dummies" Carey Nachenberg of Symantec and UCLA CS

Stanford Seminar - "Deep Learning for Dummies" Carey Nachenberg of Symantec and UCLA CS
"Deep Learning For Dummies" - Carey Nachenberg of Symantec and UCLA CS Colloquium on Computer Systems Seminar Series (EE380) presents the current research in design, implementation, analysis, and use of computer systems. Topics range from integrated circuits to operating systems and programming languages. It is free and open to the public, with new lectures each week. Learn more: http://bit.ly/WinYX5
15:08

Recommended hardware configurations for NVIDIA DIGITS (Caffe) Deep Learning Platform

Recommended hardware configurations for NVIDIA DIGITS (Caffe) Deep Learning Platform

Recommended hardware configurations for NVIDIA DIGITS (Caffe) Deep Learning Platform
An overview of Puget Systems recommended hardware configurations for NVIDIA DIGITS (Caffe) Deep Learning platform. Recommended Systems for NVIDIA DIGITS/Caffe https://www.pugetsystems.com/recommended/Recommended-Systems-for-NVIDIA-DIGITS-Caffe-174 NVIDIA DIGITS with Caffe - Performance on Pascal multi-GPU https://www.pugetsystems.com/labs/hpc/NVIDIA-DIGITS-with-Caffe---Performance-on-Pascal-multi-GPU-870/
15:50

TensorFlow Basics - Deep Learning with Neural Networks p. 2

TensorFlow Basics - Deep Learning with Neural Networks p. 2

TensorFlow Basics - Deep Learning with Neural Networks p. 2
Welcome to part two of Deep Learning with Neural Networks and TensorFlow, and part 44 of the Machine Learning tutorial series. In this tutorial, we are going to be covering some basics on what TensorFlow is, and how to begin using it. Libraries like TensorFlow and Theano are not simply deep learning libraries, they are libraries *for* deep learning. They are actually just number-crunching libraries, much like Numpy is. The difference is, however, a package like TensorFlow allows us to perform specific machine learning number-crunching operations like derivatives on huge matricies with large efficiency. We can also easily distribute this processing across our CPU cores, GPU cores, or even multiple devices like multiple GPUs. But that's not all! We can even distribute computations across a distributed network of computers with TensorFlow. So, while TensorFlow is mainly being used with machine learning right now, it actually stands to have uses in other fields, since really it is just a massive array manipulation library. What is a tensor? Up to this point in the machine learning series, we've been working mainly with vectors (numpy arrays), and a tensor can be a vector. Most simply, a tensor is an array-like object, and, as you've seen, an array can hold your matrix, your vector, and really even a scalar. Installing TensorFlow via Ubuntu and virtualbox: https://pythonprogramming.net/installing-tensorflow-machine-learning-tutorial/ https://pythonprogramming.net https://twitter.com/sentdex https://www.facebook.com/pythonprogramming.net/ https://plus.google.com/+sentdex
2:35:53

Tensorflow and deep learning - without a PhD by Martin Görner

Tensorflow and deep learning - without a PhD by Martin Görner

Tensorflow and deep learning - without a PhD by Martin Görner
Google has recently open-sourced its framework for machine learning and neural networks called Tensorflow. With this new tool, deep machine learning transitions from an area of research into mainstream software engineering. In this session, we will teach you how to choose the right neural network for your problem and how to make it behave. Familiarity with differential equations is no longer required. Instead, a couple of lines ofTensorflow Python, and a bag of "tricks of the trade" will do the job. No previous Python knowledge required. This university session will cover the basics of deep learning, without any assumptions about the level of the participants. Machine learning beginners are welcome. We will cover: - fully connected neural networks - convolutional neural networks - regularisation techniques: dropout, learning rate decay, batch normalisation - recurrent neural networks - natural language analysis, word embeddings - transfer learning - image analysis - image generation - and many examples. Martin Görner is passionate about science, technology, coding, algorithms and everything in between. He graduated from Mines Paris Tech, enjoyed his first engineering years in the computer architecture group of ST Microlectronics and then spent the next 11 years shaping the nascent eBook market, starting with the Mobipocket startup, which later became the software part of the Amazon Kindle and its mobile variants. He joined Google Developer Relations in 2011 and now focuses on parallel processing and machine learning. [ULT-2698]
41:32

The Future of Deep Learning Research

The Future of Deep Learning Research

The Future of Deep Learning Research
Back-propagation is fundamental to deep learning. Hinton (the inventor) recently said we should "throw it all away and start over". What should we do? I'll describe how back-propagation works, how its used in deep learning, then give 7 interesting research directions that could overtake back-propagation in the near term. Code for this video: https://github.com/llSourcell/7_Research_Directions_Deep_Learning Please Subscribe! And like. And comment. More learning resources: https://www.youtube.com/watch?v=q555kfIFUCM https://www.youtube.com/watch?v=h3l4qz76JhQ https://www.youtube.com/watch?v=vOppzHpvTiQ https://deeplearning4j.org/deepautoencoder https://deeplearning4j.org/glossary https://www.reddit.com/r/MachineLearning/comments/70e4ex/n_hinton_says_we_should_scrap_back_propagation/ https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/ http://kvfrans.com/generative-adversial-networks-explained/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693
12:38

Should you learn AI? | Artificial Intelligence | Machine Learning | Deep learning

Should you learn AI? | Artificial Intelligence | Machine Learning | Deep learning

Should you learn AI? | Artificial Intelligence | Machine Learning | Deep learning
Searching a image when you know the name is possible. But if you want to search for a name if you have only image or logo then what ? Google has a feature called google goggle. This feature helps you detect the name of the logo. This software works on Artificial Intelligence. Trainer: Navin Reddy visit our website : www.telusko.com facebook page : https://goo.gl/kNnJvG google plus : https://goo.gl/43Fa7i Subscribe to the channel and learn Programming in easy way. Java Tutorial for Beginners : https://goo.gl/p10QfB Scala Tutorials for Java Developers : https://goo.gl/8H1aE5 C Tutorial Playlist : https://goo.gl/8v92pu Android Tutorial for Beginners Playlist : https://goo.gl/MzlIUJ XML Tutorial : https://goo.gl/Eo79do Design Patterns in Java : https://goo.gl/Kd2MWE Java Servlet : https://goo.gl/R5nHp8 Hibernate Tutorial :https://goo.gl/N5ONYg Spring MVC Tutorial : https://goo.gl/9ubbG2 OpenShift Tutorial for Beginners : https://goo.gl/s58BQH Spring Framework with Maven : https://goo.gl/MaEluO Sql Tutorial for Beginners : https://goo.gl/x3PrTg String Handling in Java : https://goo.gl/zUdPwa Array in Java : https://goo.gl/uXTaUy Socket Programming in Java : https://goo.gl/jlMEbg Exception Handling in Java : https://goo.gl/N4NbAW Regards, Navin Reddy
36:40

Artificial Neural Network Tutorial | Deep Learning With Neural Networks | Edureka

Artificial Neural Network Tutorial | Deep Learning With Neural Networks | Edureka

Artificial Neural Network Tutorial | Deep Learning With Neural Networks | Edureka
This Edureka "Neural Network Tutorial" video (Blog: https://goo.gl/4zxMfU) will help you to understand the basics of Neural Networks and how to use it for deep learning. It explains Single layer and Multi layer Perceptron in detail. Below are the topics covered in this tutorial: 1. Why Neural Networks? 2. Motivation Behind Neural Networks 3. What is Neural Network? 4. Single Layer Percpetron 5. Multi Layer Perceptron 6. Use-Case 7. Applications of Neural Networks Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE - - - - - - - - - - - - - - How it Works? 1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each. 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders. Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course. - - - - - - - - - - - - - - Who should go for this course? The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. Business Analysts who want to understand Deep Learning (ML) Techniques 4. Information Architects who want to gain expertise in Predictive Analytics 5. Professionals who want to captivate and analyze Big Data 6. Analysts wanting to understand Data Science methodologies However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio. - - - - - - - - - - - - - - Why Learn Deep Learning With TensorFlow? TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning. Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world. Please write back to us at sales@edureka.co or call us at +91 88808 62004 for more information. Website: https://www.edureka.co/ai-deep-learni... Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
6:53

Hello World - Machine Learning Recipes #1

Hello World - Machine Learning Recipes #1

Hello World - Machine Learning Recipes #1
Six lines of Python is all it takes to write your first machine learning program! In this episode, we'll briefly introduce what machine learning is and why it's important. Then, we'll follow a recipe for supervised learning (a technique to create a classifier from examples) and code it up. Follow https://twitter.com/random_forests for updates on new episodes! Subscribe to the Google Developers: http://goo.gl/mQyv5L - Subscribe to the brand new Firebase Channel: https://goo.gl/9giPHG And here's our playlist: https://goo.gl/KewA03
21:17

Deep Learning: Keras Short Tutorial

Deep Learning: Keras Short Tutorial

Deep Learning: Keras Short Tutorial
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Foreign Secretary Boris Johnson speaking at Chatham House in London, 2 December 2016

British Foreign Secretary to visit Russia

Edit Suryaa 15 Oct 2017

Moscow, Oct 15 ... "We are planning to focus on the normalisation of Russian-British relations through dialogue ... The FCO said Britain continues its robust engagement with Russia but they have "deep differences" in particular over Syria and Ukraine . ....

photo: Creative Commons / Olaf Kosinsky

Sigmar Gabriel, SPD, at the SPD Bundestag on 19 March 2017 in Berlin

Danger of war, Germany warns after Trump's move on Iran nuclear deal

Edit Deccan Herald 14 Oct 2017

U.S. President Donald Trump refused on Friday to formally certify that Tehran was complying with the 2015 accord even though international inspectors say it is. He warned he might ultimately terminate the agreement. Reuters file photo ... U.S. President Donald Trump refused on Friday to formally certify that Tehran was complying with the 2015 accord even though international inspectors say it is ... credibility abroad. Trump has given the U.S ... ....

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Harvey Weinstein was protected for decades by the cowardice of the press

Edit The Guardian 15 Oct 2017

Peter Preston. While US publications deserve praise for breaking the story, the changing cultural climate also has a lot to do with the movie mogul’s downfall. New York Times. Peter Preston on press and broadcasting. Harvey Weinstein was protected for decades by the cowardice of the press. Peter Preston ... But Ryan Holiday in the New York Observer asks a cruel question ... His answer ... Nothing to be proud of ... ....

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Sebastian Kurz, Minister for European and International Affairs of Austria, addresses the general debate of the General Assembly’s seventy-second session

Austria Set to Elect Youngest EU Leader in Right-Wing Push

Edit The Local 15 Oct 2017

Austrians were voting on Sunday in a snap election tipped to see conservative Sebastian Kurz, 31, become the EU's youngest leader and form an alliance with the far-right, in the bloc's latest populist test ... ....

photo: Creative Commons / Hayne Palmour IV

Wildfire 'Border Fire', San Diego County, California

California 'horror' fires burn on, 40 dead in one week

Edit The Times of India 15 Oct 2017

By Heather Somerville. SANTA ROSA, Oct 15 (Reuters) - Authorities hope weaker winds will help more than 10,000 firefighters battle the deadliest blazes in California history, which have killed at least 40 people and destroyed thousands of structures in one of the state's worst natural disasters in years ... The fires have damaged or destroyed about 5,700 structures, reducing homes and businesses to ash ... GLIMMERS OF HOPE ... 20 ... RELATED ... carwow....

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Deep on October 15, 2017. Deep - 80 Impact (Deep) on 10/21/17 ... ....

NDDC to Construct 14.5 Kilometre Dual Carriage Road to Bakassi Deep Seaport

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The Niger Delta Development Commission (NDDC) has announced plans to construct a 14.5 kilometres dual carriage way from Calabar to link the proposed Bakassi Deep Seaport, as well as facilitate the realisation of the 275 kilometres Super Highway to link the proposed seaport and Katsina-Ala in Benue State....

China witnesses ‘deep, fundamental’ changes in past 5 years: communique

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China has witnessed deep and fundamental changes over the past five years, while its achievements are comprehensive and groundbreaking, according to a communique released on Oct 14 ... Many deep-rooted problems and issues that were too difficult to be resolved before have been addressed during that period, which promoted the historic changes for the cause of the Party and the country, according to the document ... (noodl. 41093531) ....

What we learned: Five takeaways from FSU's 17-10 win over Duke

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What we learned in prep football, Week 7

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There was plenty to be learned from prep football this week. That's all in Patrick Carr's notebook ... ....

Learning experience aplenty for Brian Ferentz in first season as Iowa offensive coordinator

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IOWA CITY – Midway through his first season as Iowa’s offensive coordinator, there have been a few restless nights, some success and plenty of opportunities to learn for Brian Ferentz ... ....

Austin Peay 21 Tennessee State 17: 5 things we learned

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Austin Peay defeated in-state rival Tennessee State 21-17 Saturday night at Nissan Stadium. Here are five things we learned from the game. . ... ....

Gibson won’t learn much from series – Jennings

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Ray Jennings doesn’t expect new Proteas coach Ottis Gibson to learn much about his team from the series against Bangladesh ... ....

Nebraska-Ohio State: Five things we learned, still don't know

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Five things we learned and five things we still don’t know about the Huskers coming out of Saturday's loss against Ohio State ... ....

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American actor and comedian Fred Armisen has just learned that his grandfather was a legendary dancer from Japan who, while living in Germany in the ... ....

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With several late defensive stops and an early fourth-quarter touchdown by true freshman running back Cam Akers, the Florida State football team beat Duke on Saturday afternoon to win its second game of the season. Here are three things we learned about the Seminoles (2-3, 2-2 ACC) following the... ....

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Kids had access to hundreds of years of local history on Saturday at Dig Day, the Colorado Springs Pioneers Museum's annual hands-on exploration of archaeology, paleontology, geology and mining ... "We promote events like family fun days so families feel like they can come in, relax, they can play games. You can learn about the past by doing things, not just by listening to a lecture or looking at an object ... ....

Joep Beving review: delicate melodies tap into something deep and universal

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MELBOURNE FESTIVAL. MUSIC. JOEP BEVING ★★★★. Melbourne Recital Centre. October 14 Joep Beving. Photo. Rahi Rezvani. It's not often an upright piano makes it to the big time, sitting centre-stage for a solo piano recital ... And Beving's music has a similar effect ... Yet like the best fairytales, they can bypass the conscious mind and tap into something deep and universal ... Perhaps that child was me ... ....

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Irene Chen A Beginner's Guide to Deep Learning PyCon 2016

Deep Learning with Neural Networks and TensorFlow Introduction

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How To Get Started With Machine Learning? | Two Minute Papers #51

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Artificial Neural Network Tutorial | Deep Learning With Neural Networks | Edureka

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Deep Learning: Keras Short Tutorial

Irene Chen A Beginner's Guide to Deep Learning PyCon 2016

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Deep Learning SIMPLIFIED: The Series Intro - Ep. 1

How To Get Started With Machine Learning? | Two Minute Papers #51

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How to Learn from Little Data - Intro to Deep Learning #17

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Introduction to Deep Learning part 1

Stanford Seminar - "Deep Learning for Dummies" Carey Nachenberg of Symantec and UCLA CS

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