awesome-deep-learning-resources

Incredibly fast distributed training of a CNN.

Let RNNs decide how long they compute. I would love to see how well would it combines to Neural Turing Machines. Interesting interactive visualizations on the subject can be found

Introducing multi-head self-attention neural networks with positional encoding to do sentence-level NLP without any RNN nor CNN - this paper is a must-read (also see

Batch normalization (BN): to normalize a layer's output by also summing over the entire batch, and then performing a linear rescaling and shifting of a certain trainable amount.

Better classifications with RNNs with bidirectional scanning on the time axis.

You_Again's summary/overview of deep learning, mostly about RNNs.

Convergence methods in physic engines, and applied to interaction design.

Nice animations for rotation and rotation interpolation with Quaternions, a mathematical object for handling 3D rotations.

Picturing backprop, mathematically.

Unfolding of RNN graphs is explained properly, and potential problems about gradient descent algorithms are exposed.

Understanding bias and variance in the predictions of a neural net and how to address those problems.

Simple Python demo on signal processing.

Parsey McParseface's birth, a neural syntax tree parser.

Interesting for visual animations, it is a nice intro to attention mechanisms as an example.

Grow decision trees and visualize them, infer the hidden logic behind data.

Clever trick to estimate an optimal learning rate prior any single full training.

Author of Keras - has interesting Twitter posts and innovative ideas.

Learn to slay down hyperparameter spaces automatically rather than by hand.

arXiv browser with TF/IDF features.

An awesome list for Neuraxle, a ML Framework for coding clean production-level ML pipelines.

This is a hub similar to Hacker News, but specific to data science.

Maybe how I discovered ML - Interesting trends appear on that site way before they get to be a big deal.

This is a Korean search engine - best used with Google Translate, ironically. Surprisingly, sometimes deep learning search results and comprehensible advanced math content shows up more easily there than on Google search.

A talk for a reading group on attention mechanisms (Paper: Neural Machine Translation by Jointly Learning to Align and Translate).

Yet another YouTube playlist I composed, this time about various CS topics.

A list of videos about deep learning that I found interesting or useful, this is a mix of a bit of everything.

Andrew Ng interviews Geoffrey Hinton, who talks about his research and breaktroughs, and gives advice for students.

A primer on how to structure your Machine Learning projects when using Jupyter Notebooks.

A YouTube playlist I composed about DFT/FFT, STFT and the Laplace transform - I was mad about my software engineering bachelor not including signal processing classes (except a bit in the quantum physics class).

An awesome list of public datasets.

Neural Turing Machine TensorFlow implementation.

Many interesting neural network architectures are implemented by the Korean guy Taehoon Kim, A.K.A. carpedm20.

Learn the good design patterns to use for doing Machine Learning the good way, by practicing.

This could be used for a chatbot.

Transfer learning tutorial in TensorFlow for vision from high-level embeddings of a pretrained CNN, AlexNet 2012.

Get back to the basics you fool! Learn how to do Clean Code for your career. This is by far the best book I've read even if this list is related to Deep Learning.

Learn how to be professional as a coder and how to interact with your manager. This is important for any coding career.

Yet halfway through the book, it contains satisfying math content on how to think about actual deep learning.

The audio version is nice to listen to while commuting. This book is motivating about reverse-engineering the mind and thinking on how to code AI.

This book covers many of the core concepts behind neural networks and deep learning.

Some books listed here are less related to deep learning but are still somehow relevant to this list.

Good intermediate to advanced-level course covering high-level deep learning concepts, I found it helps to get creative once the basics are acquired.

The most richly dense, accelerated course on the topic of Deep Learning & Recurrent Neural Networks (scroll at the end).

New series of 5 Deep Learning courses by Andrew Ng, now with Python rather than Matlab/Octave, and which leads to a

I created this richely dense course on Deep Learning and Recurrent Neural Networks.

This is a class given by Philippe Giguère, Professor at University Laval. I especially found awesome its rare visualization of the multi-head attention mechanism, which can be contemplated at the

Renown entry-level online class with