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Smolensky, Paul. Information processing in dynamical systems: Foundations of harmony theory. No. CU-CS-321-86. COLORADO UNIV AT BOULDER DEPT OF COMPUTER SCIENCE, 1986. (Link)
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Salakhutdinov, Ruslan, Andriy Mnih, and Geoffrey Hinton. Restricted Boltzmann machines for collaborative filtering. Proceedings of the 24th international conference on Machine learning. ACM, 2007.(Link)
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Hinton, Geoffrey. A practical guide to training restricted Boltzmann machines. Momentum 9.1 (2010): 926.(Link)
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If you are interested in math, this is a good tutorial: http://deeplearning.net/tutorial/rbm.html#rbm
- Preface
- TensorFlow - An Introduction
- Introduction
- Installing TensorFlow
- Hello world in TensorFlow
- Understanding the TensorFlow program structure
- Working with constants, variables, and placeholders
- Performing matrix manipulations using TensorFlow
- Using a data flow graph
- Migrating from 0.x to 1.x
- Using XLA to enhance computational performance
- Invoking CPU/GPU devices
- TensorFlow for Deep Learning
- Different Python packages required for DNN-based problems
- Regression
- Neural Networks - Perceptron
- Convolutional Neural Networks
- Advanced Convolutional Neural Networks
- Introduction
- Creating a ConvNet for Sentiment Analysis
- Inspecting what filters a VGG pre-built network has learned
- Classifying images with VGGNet, ResNet, Inception, and Xception
- Recycling pre-built Deep Learning models for extracting features
- Very deep InceptionV3 Net used for Transfer Learning
- Generating music with dilated ConvNets, WaveNet, and NSynth
- Answering questions about images (Visual Q&A)
- Classifying videos with pre-trained nets in six different ways
- Recurrent Neural Networks
- Introduction
- Neural machine translation - training a seq2seq RNN
- Neural machine translation - inference on a seq2seq RNN
- All you need is attention - another example of a seq2seq RNN
- Learning to write as Shakespeare with RNNs
- Learning to predict future Bitcoin value with RNNs
- Many-to-one and many-to-many RNN examples
- Unsupervised Learning
- Autoencoders
- Reinforcement Learning
- Mobile Computation
- Introduction
- Installing TensorFlow mobile for macOS and Android
- Playing with TensorFlow and Android examples
- Installing TensorFlow mobile for macOS and iPhone
- Optimizing a TensorFlow graph for mobile devices
- Profiling a TensorFlow graph for mobile devices
- Transforming a TensorFlow graph for mobile devices
- Generative Models and CapsNet
- Distributed TensorFlow and Cloud Deep Learning
- Introduction
- Working with TensorFlow and GPUs
- Playing with Distributed TensorFlow: multiple GPUs and one CPU
- Playing with Distributed TensorFlow: multiple servers
- Training a Distributed TensorFlow MNIST classifier
- Working with TensorFlow Serving and Docker
- Running Distributed TensorFlow on Google Cloud (GCP) with Compute Engine
- Running Distributed TensorFlow on Google CloudML
- Running Distributed TensorFlow on Microsoft Azure
- Running Distributed TensorFlow on Amazon AWS
- Learning to Learn with AutoML (Meta-Learning)
- TensorFlow Processing Units
See also
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