- published: 27 Jun 2017
- views: 71488
Neural network(s) may refer to:
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.
RNN may refer to:
In machine learning and cognitive science, artificial neural networks (ANNs) are a family of models inspired by biological neural networks (the central nervous systems of animals, in particular the brain) and are used to estimate or approximate functions that can depend on a large number of inputs and are generally unknown. Artificial neural networks are generally presented as systems of interconnected "neurons" which exchange messages between each other. The connections have numeric weights that can be tuned based on experience, making neural nets adaptive to inputs and capable of learning.
For example, a neural network for handwriting recognition is defined by a set of input neurons which may be activated by the pixels of an input image. After being weighted and transformed by a function (determined by the network's designer), the activations of these neurons are then passed on to other neurons. This process is repeated until finally, an output neuron is activated. This determines which character was read.
Deep or The Deep may refer to:
A gentle walk through how they work and how they are useful. Some other helpful resources: RNN and LSTM slides: http://bit.ly/2sO00ZC Luis Serrano's Friendly Intro to RNNs: https://youtu.be/UNmqTiOnRfg How neural networks work video: https://youtu.be/ILsA4nyG7I0 Chris Olah's tutorial: http://bit.ly/2seO9VI Andrej Karpathy's blog post: http://bit.ly/1K610Ie Andrej Karpathy's RNN code: http://bit.ly/1TNCiT9 Andrej Karpathy's CS231n lecture: http://bit.ly/2tijgQ9 DeepLearning4J tutorial: https://deeplearning4j.org/lstm RNN/LSTM blog post: https://brohrer.github.io/how_rnn_lstm_work.html Data Science and Robots blog: https://brohrer.github.io/blog.html
Our previous discussions of deep net applications were limited to static patterns, but how can a net decipher and label patterns that change with time? For example, could a net be used to scan traffic footage and immediately flag a collision? Through the use of a recurrent net, these real-time interactions are now possible. Deep Learning TV on Facebook: https://www.facebook.com/DeepLearningTV/ Twitter: https://twitter.com/deeplearningtv The Recurrent Neural Net (RNN) is the brainchild of Juergen Schmidhuber and Sepp Hochreiter. The three deep nets we’ve seen thus far – MLP, DBN, and CNN – are known as feedforward networks since a signal moves in only one direction across the layers. In contrast, RNNs have a feedback loop where the net’s output is fed back into the net along with the nex...
In this deep learning with TensorFlow tutorial, we cover how to implement a Recurrent Neural Network, with an LSTM (long short term memory) cell with the MNIST dataset. https://pythonprogramming.net https://twitter.com/sentdex https://www.facebook.com/pythonprogramming.net/ https://plus.google.com/+sentdex
Recurrent networks let us learn from sequential data (time series, music, audio, video frames, etc ). We're going to build one from scratch in numpy (including backpropagation) to generate a sequence of words in the style of Franz Kafka. Code for this video: https://github.com/llSourcell/recurrent_neural_network Please Subscribe! And like. And comment. That's what keeps me going. More learning resources: https://www.youtube.com/watch?v=hWgGJeAvLws https://www.youtube.com/watch?v=cdLUzrjnlr4 https://medium.freecodecamp.org/dive-into-deep-learning-with-these-23-online-courses-bf247d289cc0 http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/ https://deeplearning4j.org/lstm.html http://karpathy.github.io/2015/05/21/rnn-effectiveness/ Join us in the...
In this video, I explain the basics of recurrent neural networks. Then we code our own RNN in 80 lines of python (plus white-space) that predicts the sum of two binary numbers after training. Code for this video: https://github.com/llSourcell/recurrent_neural_net_demo I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ Thank @iamtrask for a great RNN article: https://iamtrask.github.io/2015/11/15/anyone-can-code-lstm/ and this piece by Karpathy on RNN's deserves some sort of award: http://karpathy.github.io/2015/05/21/rnn-effectiveness/ Another great RNN article: http://nikhilbuduma.com/2015/01/11/a-deep-dive-into-recurrent-neural-networks/ Tensorflow RNNs: https://www.tensorflow.org/versions/r0.10/tutorials/recurrent/index.html Thanks so much for watchin...
RNN recurrent neural networks 在序列化的预测当中是很有优势的. 我很先看看 RNN 是怎么工作的. RNN 简介: https://www.youtube.com/watch?v=EEtf4kNsk7Q&list;=PLXO45tsB95cIFm8Y8vMkNNPPXAtYXwKin&index;=4 Google CNN tutorial: https://classroom.udacity.com/courses/ud730/lessons/6377263405/concepts/64063017560923# Google RNN tutorial:https://classroom.udacity.com/courses/ud730/lessons/6378983156/concepts/63770919610923# Tensflow playlist: https://www.youtube.com/playlist?list=PLXO45tsB95cKI5AIlf5TxxFPzb-0zeVZ8 我创建的学习网站: http://morvanzhou.github.io/tutorials/ 微博更新: 莫烦python QQ 机器学习讨论群: 531670665
Video from Coursera - University of Toronto - Course: Neural Networks for Machine Learning: https://www.coursera.org/course/neuralnets
In this talk, Eugene Brevdo discusses the creation of flexible and high-performance sequence-to-sequence models. He covers reading and batching sequence data, the RNN API, fully dynamic calculation, fused RNN cells for optimizations for special cases, and dynamic decoding. Visit the TensorFlow website for all session recordings: https://goo.gl/bsYmza Subscribe to the Google Developers channel at http://goo.gl/mQyv5L
In this Deep Learning with TensorFlow tutorial, we cover the basics of the Recurrent Neural Network, along with the LSTM (Long Short Term Memory) cell, which is a very common RNN cell used. https://pythonprogramming.net https://twitter.com/sentdex https://www.facebook.com/pythonprogramming.net/ https://plus.google.com/+sentdex
We motivate why recurrent neural networks are important for dealing with sequence data and review LSTMs and GRU architectures.
A live demo of a deep learning system developed at Cambridge Consultants to classify piano music as it's played. The audio from the piano is routed to two systems in parallel: (1) Deep learning (blue path at the top of the picture) (2) Conventional "handed-coded" classifier (orange path at the bottom of the picture) The deep learning system has been trained on about 200 hours of 4 genres of piano from practice sessions, old recordings etc. It has not "heard" the test pieces during training, nor the piano used in the demo, nor the piano players used in the demo. It has to deal with a number of unknowns such as the level of stretch tuning on the piano, its tone, and the pianist's playing style. The deep learning system is way more accurate than our best efforts at handing coding a classif...
수업 웹페이지: https://hunkim.github.io/ml/ TF KR그룹: https://www.facebook.com/groups/TensorFlowKR/ 소스예제: https://github.com/nlintz/TensorFlow-Tutorials
This Edureka Recurrent Neural Networks tutorial video (Blog: https://goo.gl/4zxMfU) will help you in understanding why we need Recurrent Neural Networks (RNN) and what exactly it is. It also explains few issues with training a Recurrent Neural Network and how to overcome those challenges using LSTMs. The last section includes a use-case of LSTM to predict the next word using a sample short story Below are the topics covered in this tutorial: 1. Why Not Feedforward Networks? 2. What Are Recurrent Neural Networks? 3. Training A Recurrent Neural Network 4. Issues With Recurrent Neural Networks - Vanishing And Exploding Gradient 5. Long Short-Term Memory Networks (LSTMs) 6. LSTM Use-Case Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Deep...
This lecture is about most popular RNN cells: - vanilla RNN - GRU - LSTM cell - LSTM with peephole connections. Intuition, what’s inside, how it works, advantages and potential problems. Link to slides: https://goo.gl/XodLUU
We combine GRU-RNNs with CNNs for robust action recognition based on 3D voxel and tracking data of human movement. Our system reaches a classification accuracy of over 93%.
Video from Coursera - University of Toronto - Course: Neural Networks for Machine Learning: https://www.coursera.org/course/neuralnets
循环神经网络简介. 今天我们会来聊聊在语言分析, 序列化数据中穿梭自如的循环神经网络 RNN. Tensorflow RNN1: https://www.youtube.com/watch?v=i-cd3wzsHtw&index;=23&list;=PLXO45tsB95cKI5AIlf5TxxFPzb-0zeVZ8 Tensorflow RNN lstm: https://www.youtube.com/watch?v=IASyrQamTQk&index;=24&list;=PLXO45tsB95cKI5AIlf5TxxFPzb-0zeVZ8 RNN 作曲链接: http://www.hexahedria.com/2015/08/03/composing-music-with-recurrent-neural-networks/ 机器学习-简介系列 播放列表: https://www.youtube.com/playlist?list=PLXO45tsB95cIFm8Y8vMkNNPPXAtYXwKin 我创建的学习网站: http://morvanzhou.github.io/tutorials/ 微博更新: 莫烦python QQ 机器学习讨论群: 531670665
when will i learn to be alone
how can i learn to let go of you
everyone can see me
but i can't see myself
have you seen the key that
leads to me
'cause i need to find myself
i'm running out
i'm running out of time
to find myself
why do i suck at putting smiles on their faces
why am i only funny to myself
how come no one understands what i say
but i thought i didn't know myself
i hate everyone
only on certain days
i think you're all
annoyed by me
so i'll leave, and find myself