Awesome-Deep-Learning-Resources

Renown entry-level online class with . Taught by: Andrew Ng, Associate Professor, Stanford University; Chief Scientist, Baidu; Chairman and Co-founder, Coursera

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

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

Interesting class for acquiring basic knowledge of machine learning applied to trading and some AI and finance concepts. I especially liked the section on Q-Learning

Interesting class about neural networks available online for free by Hugo Larochelle, yet I have watched a few of those videos

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

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

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

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

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

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

MUST READ post by Andrej Karpathy - this is what motivated me to learn RNNs, it demonstrates what it can achieve in the most basic form of NLP

Fresh look on how neurons map information

Explains the LSTM cells' inner workings, plus, it has interesting links in conclusion

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

Awesome for doing clustering on audio - post by an intern at Spotify

Parsey McParseface's birth, a neural syntax tree parser

Very interesting CNN architecture (e.g.: the inception-style convolutional layers is promising and efficient in terms of reducing the number of parameters)

Realistic talking machines: perfect voice generation

Author of Keras - has interesting Twitter posts and innovative ideas

Thought provoking article about the future of the brain and brain-computer interfaces

Easily manage huge files in your private Git projects

François Chollet's thoughts on the future of deep learning

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

Learn to slay down hyperparameter spaces automatically rather than by hand

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

Good for understanding the "Attention Is All You Need" (AIAYN) paper

Also good for understanding the "Attention Is All You Need" (AIAYN) paper

SOTA across many NLP tasks from unsupervised pretraining on huge corpus

All hail NLP's ImageNet moment

Understand the different approaches used for NLP's ImageNet moment

Not only the SOLID principles are needed for doing clean code, but the furtherless known REP, CCP, CRP, ADP, SDP and SAP principles are very important for developping huge software that must be bundled in different separated packages

Data is not to be overlooked, and communication between teams and data scientists is important to integrate solutions properly

Focus on clear business objectives, avoid pivots of algorithms unless you have really clean code, and be able to know when what you coded is "good enough"

The SOLID principles applied to Machine Learning

TensorFlow's GitHub repository

Most known deep learning framework, both high-level and low-level while staying flexible

skflow

TensorFlow wrapper à la scikit-learn

Keras is another intersting deep learning framework like TensorFlow, it is mostly high-level

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

carpedm20/NTM-tensorflow

Neural Turing Machine TensorFlow implementation

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

LSTM for Human Activity Recognition (HAR)

Tutorial of mine on using LSTMs on time series for classification

Deep stacked residual bidirectional LSTMs for HAR

Improvements on the previous project

Sequence to Sequence (seq2seq) Recurrent Neural Network (RNN) for Time Series Prediction

Tutorial of mine on how to predict temporal sequences of numbers - that may be multichannel

Hyperopt for a Keras CNN on CIFAR-100

Auto (meta) optimizing a neural net (and its architecture) on the CIFAR-100 dataset

GitHub is full of nice code samples & projects

Smoothly Blend Image Patches

Smooth patch merger for

Self Governing Neural Networks (SGNN): the Projection Layer

With this, you can use words in your deep learning models without training nor loading embeddings

Neuraxle

Neuraxle is a Machine Learning (ML) library for building neat pipelines, providing the right abstractions to both ease research, development, and deployment of your ML applications

Clean Machine Learning, a Coding Kata

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

This could be used for a chatbot

Question answering dataset that can be explored online, and a list of models performing well on that dataset

Huge free English speech dataset with balanced genders and speakers, that seems to be of high quality

Awesome Public Datasets

An awesome list of public datasets

A Python framework to benchmark your sentence representations on many datasets (NLP tasks)

Another Python framework to benchmark your sentence representations on many datasets (NLP tasks)

A visual proof that neural nets can compute any function

Exposing backprop's caveats and the importance of knowing that while training models

Picturing backprop, mathematically

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

Visualize how different optimizers interacts with a saddle points

Visualize how different optimizers interacts with an almost flat landscape

Okay, I already listed Andrew NG's Coursera class above, but this video especially is quite pertinent as an introduction and defines the gradient descent algorithm

What follows from the previous video: now add intuition

How to adjust the learning rate of a neural network

A good explanation of overfitting and how to address that problem

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

Appearance of the incredible SELU activation function

RNN as an optimizer: introducing the L2L optimizer, a meta-neural network

New look on Fourier analysis

Animations dealing with complex numbers and wave equations

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

Filtering signal, plotting the STFT and the Laplace transform

Simple Python demo on signal processing

Better classifications with RNNs with bidirectional scanning on the time axis

Two networks in one combined into a seq2seq (sequence to sequence) Encoder-Decoder architecture. RNN Encoder–Decoder with 1000 hidden units. Adadelta optimizer

4 stacked LSTM cells of 1000 hidden size with reversed input sentences, and with beam search, on the WMT’14 English to French dataset

Nice recursive models using word-level LSTMs on top of a character-level CNN using an overkill amount of GPU power

Interesting overview of the subject of NMT, I mostly read part 8 about RNNs with attention as a refresher

Basically, residual connections can be better than stacked RNNs in the presented case of sentiment analysis

Nice for photoshop-like "content aware fill" to fill missing patches in images

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

AlexNet, 2012 ILSVRC, breakthrough of the ReLU activation function

For the "deconvnet layer"

ELU activation function for CIFAR vision tasks

Interesting idea of stacking multiple 3x3 conv+ReLU before pooling for a bigger filter size with just a few parameters. There is also a nice table for "ConvNet Configuration"

GoogLeNet: Appearance of "Inception" layers/modules, the idea is of parallelizing conv layers into many mini-conv of different size with "same" padding, concatenated on depth

Highway networks: residual connections

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

The U-Net is an encoder-decoder CNN that also has skip-connections, good for image segmentation at a per-pixel level

Very deep residual layers with batch normalization layers - a.k.a. "how to overfit any vision dataset with too many layers and make any vision model work properly at recognition given enough data"

For improving GoogLeNet with residual connections

Epic raw voice/music generation with new architectures based on dilated causal convolutions to capture more audio length

3D-GANs for 3D model generation and fun 3D furniture arithmetics from embeddings (think like word2vec word arithmetics with 3D furniture representations)

Incredibly fast distributed training of a CNN

Best Paper Award at CVPR 2017, yielding improvements on state-of-the-art performances on CIFAR-10, CIFAR-100 and SVHN datasets, this new neural network architecture is named DenseNet

Merges the ideas of the U-Net and the DenseNet, this new neural network is especially good for huge datasets in image segmentation

Use a distance metric in the loss to determine to which class does an object belongs to from a few examples

Outstanding for letting a neural network learn an algorithm with seemingly good generalization over long time dependencies. Sequences recall problem

LSTMs' attention mechanisms on CNNs feature maps does wonders

A very interesting and creative work about textual question answering, what a breakthrough, there is something to do with that

Exploring different approaches to attention mechanisms

Interesting way of doing one-shot learning with low-data by using an attention mechanism and a query to compare an image to other images for classification

In 2016: stacked residual LSTMs with attention mechanisms on encoder/decoder are the best for NMT (Neural Machine Translation)

Improvements on differentiable memory based on NTMs: now it is the Differentiable Neural Computer (DNC)

That yields intuition about the boundaries of what works for doing NMT within a framed seq2seq problem formulation

A used as a vocoder can be conditioned on generated Mel Spectrograms from the Tacotron 2 LSTM neural network with attention to generate neat audio from text

(AIAYN) - 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 and of the paper)

This paper is the sequel to the ProjectionNet just above. The SGNN is elaborated on the ProjectionNet, and the optimizations are detailed more in-depth (also see my and watch )

Classify a new example from a list of other examples (without definitive categories) and with low-data per classification task, but lots of data for lots of similar classification tasks - it seems better than siamese networks. To sum up: with Matching Networks, you can optimize directly for a cosine similarity between examples (like a self-attention product would match) which is passed to the softmax directly. I guess that Matching Networks could probably be used as with negative-sampling softmax training in word2vec's CBOW or Skip-gram without having to do any context embedding lookups

Generalize properly how Tensors work, yet just watching a few videos already helps a lot to grasp the concepts

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

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)

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

Siraj has entertaining, fast-paced video tutorials about deep learning

Interesting and shallow overview of some research papers, for example about WaveNet or Neural Style Transfer

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

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

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

arXiv browser with TF/IDF features

Awesome Neuraxle

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