Machine Learning Resources

Neural Networks

• Neural Networks and Deep Learning

Recurrent Neural Networks

• Recurrent Neural Networks Tutorial, Part 1 – Introduction to RNNs
• Recurrent Neural Networks Tutorial, Part 2 – Implementing a RNN with Python, Numpy and Theano
• Recurrent Neural Networks Tutorial, Part 3 – Backpropagation Through Time and Vanishing Gradients
• Recurrent Neural Network Tutorial, Part 4 – Implementing a GRU/LSTM RNN with Python and Theano
• Understanding LSTM Networks

Deep Learning

Deep Learning Book (Ian Goodfellow, Yoshua Bengio and Aaron Courville) (link)

1. Introduction
2. Linear Algebra
3. Probability and Information Theory
4. Numerical Computation
5. Machine Learning Basics
6. Deep Feedforward Networks
7. Regularization
8. Optimization for Training Deep Models
9. Convolutional Networks
10. Sequence Modeling: Recurrent and Recursive Nets
11. Practical Methodology
12. Applications

NLP

Deep Learning for NLP (Stanford CS224)

• Lecture Notes 1
• Lecture Notes 2
• Lecture Notes 3
• Lecture Notes 4

Torch

Machine Learning (University of Oxford)

• Introduction to Lua and Torch
• Linear models
• Classifying digits and tuning optimizers
• Implementing your own layer
• nngraph
• LSTMs for language modelling

Theano

• theano-01-basics-key
• theano-02-advanced-key
• theano-03-internals