Wiki

Welcome to our Wiki. You may find links to some useful introductory (or advanced) resources on this page.

Neural networks:

• Neural Networks and Deep Learning – Introductory book by Michael Nielsen
• Backprop – great explanation of backpropagation algorithm
• NN ZOO – evolution of neural networks
• Playground – train and visualize neural net in browser
• Neural Nets – MOOC by Geoffrey Hinton
• Machine Learning – MOOC by Andrew Ng
• Bay Area DL School 2016 or Individual Talks
• Top10 Deep Learning Tips by Arno Candel
• My Neural Network isn’t working! What should I do?
• Recipe for Training Neural Networks by Andrej Karpathy
• Gradient Descent Optimization algorithms
• Structuring ML Projects by Andrew Ng (DeepLearning.AI)

NLP:

• Stanford NLP Course videos
• Recent Trends in Deep NLP – survey of various techniques, tasks and resources used in deep NLP.
• A Primer on Neural Network Models for Natural Language Processing – introduction to neural networks in NLP context, thorough explanation of important concepts. Especially useful for beginners.
• NLP textbook from Georgia Tech – includes information about many NLP tasks. Work in progress.

Relevant blogs (cherrypicked):

• http://colah.github.io/
• http://karpathy.github.io/
• https://adeshpande3.github.io/
• http://www.cleverhans.io/

Deep Learning books/papers/resources:

• DL Reading Roadmap – structured list of highly relevant papers
• Awesome DL papers – curated list of the most cited deep learning papers

TensorFlow:

• Get started – official introduction
• Udacity DeepLearn – MOOC by Google – intro to DL with TensorFlow
• Tutorials – list of TF model implementations and tutorials
• TF Dev Summit 2017 – latest features, demos, tutorials, future plans
• TensorFlow Examples – demonstrative implementations of various NN models in TensorFlow