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