DL&RNN Course
I created this richely dense course on Deep Learning and Recurrent Neural Networks.
I created this richely dense course on Deep Learning and Recurrent Neural Networks.
Awesome Deep Learning Resources
Rough list of my favorite deep learning resources, useful for revisiting topics or for reference. I have got through all of the content listed there, carefully. - Guillaume Chevalier
This page lists names, links and short descriptions. The original list on GitHub is the source and belongs to its authors.
Online Classes
Machine Learning by Andrew Ng on Coursera
Renown entry-level online class with certificate. Taught by: Andrew Ng, Associate Professor, Stanford University; Chief Scientist, Baidu; Chairman and Co-founder, Coursera.
In 11 listsDetails
Deep Learning Specialization by Andrew Ng on Coursera
New series of 5 Deep Learning courses by Andrew Ng, now with Python rather than Matlab/Octave, and which leads to a specialization certificate.
In 6 listsDetails
Deep Learning by Google
Good intermediate to advanced-level course covering high-level deep learning concepts, I found it helps to get creative once the basics are acquired.
In 2 lists
Machine Learning for Trading by Georgia Tech
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.
In 2 lists
Neural networks class by Hugo Larochelle, Université de Sherbrooke
Interesting class about neural networks available online for free by Hugo Larochelle, yet I have watched a few of those videos.
In 2 lists
GLO-4030/7030 Apprentissage par réseaux de neurones profonds
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 slide 28 of week 13's class.
Deep Learning & Recurrent Neural Networks (DL&RNN)
The most richly dense, accelerated course on the topic of Deep Learning & Recurrent Neural Networks (scroll at the end).
Books
Clean Code
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.
Clean Coder
Learn how to be professional as a coder and how to interact with your manager. This is important for any coding career.
How to Create a Mind
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.
Neural Networks and Deep Learning
This book covers many of the core concepts behind neural networks and deep learning.
In 9 listsDetails
Deep Learning - An MIT Press book
Yet halfway through the book, it contains satisfying math content on how to think about actual deep learning.
In 3 lists
Some other books I have read
Some books listed here are less related to deep learning but are still somehow relevant to this list.
Posts and Articles
Predictions made by Ray Kurzweil
List of mid to long term futuristic predictions made by Ray Kurzweil.
The Unreasonable Effectiveness of Recurrent Neural Networks
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.
In 3 lists
Neural Networks, Manifolds, and Topology
Fresh look on how neurons map information.
Understanding LSTM Networks
Explains the LSTM cells' inner workings, plus, it has interesting links in conclusion.
In 2 lists
Attention and Augmented Recurrent Neural Networks
Interesting for visual animations, it is a nice intro to attention mechanisms as an example.
In 2 lists
Recommending music on Spotify with deep learning
Awesome for doing clustering on audio - post by an intern at Spotify.
Announcing SyntaxNet: The World’s Most Accurate Parser Goes Open Source
Parsey McParseface's birth, a neural syntax tree parser.
Improving Inception and Image Classification in TensorFlow
Very interesting CNN architecture (e.g.: the inception-style convolutional layers is promising and efficient in terms of reducing the number of parameters).
WaveNet: A Generative Model for Raw Audio
Realistic talking machines: perfect voice generation.
François Chollet's Twitter
Author of Keras - has interesting Twitter posts and innovative ideas.
In 2 lists
Neuralink and the Brain’s Magical Future
Thought provoking article about the future of the brain and brain-computer interfaces.
Migrating to Git LFS for Developing Deep Learning Applications with Large Files
Easily manage huge files in your private Git projects.
In 2 lists
The future of deep learning
François Chollet's thoughts on the future of deep learning.
Discover structure behind data with decision trees
Grow decision trees and visualize them, infer the hidden logic behind data.
In 2 lists
Hyperopt tutorial for Optimizing Neural Networks’ Hyperparameters
Learn to slay down hyperparameter spaces automatically rather than by hand.
In 2 lists
Estimating an Optimal Learning Rate For a Deep Neural Network
Clever trick to estimate an optimal learning rate prior any single full training.
The Annotated Transformer
Good for understanding the "Attention Is All You Need" (AIAYN) paper.
In 2 lists
The Illustrated Transformer
Also good for understanding the "Attention Is All You Need" (AIAYN) paper.
In 2 lists
Improving Language Understanding with Unsupervised Learning
SOTA across many NLP tasks from unsupervised pretraining on huge corpus.
In 2 lists
NLP's ImageNet moment has arrived
All hail NLP's ImageNet moment.
In 2 lists
The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning)
Understand the different approaches used for NLP's ImageNet moment.
In 3 lists
Uncle Bob's Principles Of OOD
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.
Why do 87% of data science projects never make it into production?
Data is not to be overlooked, and communication between teams and data scientists is important to integrate solutions properly.
The real reason most ML projects fail
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".
SOLID Machine Learning
The SOLID principles applied to Machine Learning.
Practical Resources >Librairies and Implementations
Neuraxle, a framwework for machine learning pipelines
The best framework for structuring and deploying your machine learning projects, and which is also compatible with most framework (e.g.: Scikit-Learn, TensorFlow, PyTorch, Keras, and so forth).
In 4 listsDetails
TensorFlow's GitHub repository
Most known deep learning framework, both high-level and low-level while staying flexible.
In 23 listsDetails
skflow
TensorFlow wrapper à la scikit-learn.
In 2 lists
Keras
Keras is another intersting deep learning framework like TensorFlow, it is mostly high-level.
In 8 listsDetails
carpedm20's repositories
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.
In 3 lists
Deep learning for lazybones
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.
In 4 lists
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.
In 4 lists
Hyperopt for a Keras CNN on CIFAR-100
Auto (meta) optimizing a neural net (and its architecture) on the CIFAR-100 dataset.
ML / DL repositories I starred
GitHub is full of nice code samples & projects.
Smoothly Blend Image Patches
Smooth patch merger for semantic segmentation with a U-Net.
Self Governing Neural Networks (SGNN): the Projection Layer
With this, you can use words in your deep learning models without training nor loading embeddings.
Clean Machine Learning, a Coding Kata
Learn the good design patterns to use for doing Machine Learning the good way, by practicing.
Practical Resources >Some Datasets
UCI Machine Learning Repository
TONS of datasets for ML.
Cornell Movie--Dialogs Corpus
This could be used for a chatbot.
In 2 lists
SQuAD The Stanford Question Answering Dataset
Question answering dataset that can be explored online, and a list of models performing well on that dataset.
In 4 listsDetails
LibriSpeech ASR corpus
Huge free English speech dataset with balanced genders and speakers, that seems to be of high quality.
In 2 lists
Awesome Public Datasets
An awesome list of public datasets.
In 3 lists
SentEval: An Evaluation Toolkit for Universal Sentence Representations
A Python framework to benchmark your sentence representations on many datasets (NLP tasks).
ParlAI: A Dialog Research Software Platform
Another Python framework to benchmark your sentence representations on many datasets (NLP tasks).
Other Math Theory >Gradient Descent Algorithms & Optimization Theory
Neural Networks and Deep Learning, ch.2
Overview on how does the backpropagation algorithm works.
Neural Networks and Deep Learning, ch.4
A visual proof that neural nets can compute any function.
Yes you should understand backprop
Exposing backprop's caveats and the importance of knowing that while training models.
Artificial Neural Networks: Mathematics of Backpropagation
Picturing backprop, mathematically.
Deep Learning Lecture 12: Recurrent Neural Nets and LSTMs
Unfolding of RNN graphs is explained properly, and potential problems about gradient descent algorithms are exposed.
Gradient descent algorithms in a saddle point
Visualize how different optimizers interacts with a saddle points.
Gradient descent algorithms in an almost flat landscape
Visualize how different optimizers interacts with an almost flat landscape.
Gradient Descent
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.
Gradient Descent: Intuition
What follows from the previous video: now add intuition.
Gradient Descent in Practice 2: Learning Rate
How to adjust the learning rate of a neural network.
The Problem of Overfitting
A good explanation of overfitting and how to address that problem.
Diagnosing Bias vs Variance
Understanding bias and variance in the predictions of a neural net and how to address those problems.
Self-Normalizing Neural Networks
Appearance of the incredible SELU activation function.
Learning to learn by gradient descent by gradient descent
RNN as an optimizer: introducing the L2L optimizer, a meta-neural network.
Other Math Theory >Complex Numbers & Digital Signal Processing
Window Functions
Wikipedia page that lists some of the known window functions - note that the Hann-Poisson window is specially interesting for greedy hill-climbing algorithms (like gradient descent for example).
MathBox, Tools for Thought Graphical Algebra and Fourier Analysis
New look on Fourier analysis.
How to Fold a Julia Fractal
Animations dealing with complex numbers and wave equations.
In 2 lists
Animate Your Way to Glory, Math and Physics in Motion
Convergence methods in physic engines, and applied to interaction design.
Animate Your Way to Glory - Part II, Math and Physics in Motion
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.
In 2 lists
Papers >Recurrent Neural Networks
Deep Learning in Neural Networks: An Overview
You_Again's summary/overview of deep learning, mostly about RNNs.
Bidirectional Recurrent Neural Networks
Better classifications with RNNs with bidirectional scanning on the time axis.
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
Two networks in one combined into a seq2seq (sequence to sequence) Encoder-Decoder architecture. RNN Encoder–Decoder with 1000 hidden units. Adadelta optimizer.
Sequence to Sequence Learning with Neural Networks
4 stacked LSTM cells of 1000 hidden size with reversed input sentences, and with beam search, on the WMT’14 English to French dataset.
In 2 lists
Exploring the Limits of Language Modeling
Nice recursive models using word-level LSTMs on top of a character-level CNN using an overkill amount of GPU power.
Neural Machine Translation and Sequence-to-sequence Models: A Tutorial
Interesting overview of the subject of NMT, I mostly read part 8 about RNNs with attention as a refresher.
Exploring the Depths of Recurrent Neural Networks with Stochastic Residual Learning
Basically, residual connections can be better than stacked RNNs in the presented case of sentiment analysis.
Pixel Recurrent Neural Networks
Nice for photoshop-like "content aware fill" to fill missing patches in images.
Adaptive Computation Time for Recurrent Neural Networks
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 here.
Papers >Convolutional Neural Networks
What is the Best Multi-Stage Architecture for Object Recognition?
Awesome for the use of "local contrast normalization".
ImageNet Classification with Deep Convolutional Neural Networks
AlexNet, 2012 ILSVRC, breakthrough of the ReLU activation function.
Visualizing and Understanding Convolutional Networks
For the "deconvnet layer".
Fast and Accurate Deep Network Learning by Exponential Linear Units
ELU activation function for CIFAR vision tasks.
Very Deep Convolutional Networks for Large-Scale Image Recognition
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".
Going Deeper with Convolutions
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
Highway networks: residual connections.
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
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.
U-Net: Convolutional Networks for Biomedical Image Segmentation
The U-Net is an encoder-decoder CNN that also has skip-connections, good for image segmentation at a per-pixel level.
Deep Residual Learning for Image Recognition
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".
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
For improving GoogLeNet with residual connections.
WaveNet: a Generative Model for Raw Audio
Epic raw voice/music generation with new architectures based on dilated causal convolutions to capture more audio length.
Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
3D-GANs for 3D model generation and fun 3D furniture arithmetics from embeddings (think like word2vec word arithmetics with 3D furniture representations).
Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
Incredibly fast distributed training of a CNN.
Densely Connected Convolutional Networks
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.
The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation
Merges the ideas of the U-Net and the DenseNet, this new neural network is especially good for huge datasets in image segmentation.
Prototypical Networks for Few-shot Learning
Use a distance metric in the loss to determine to which class does an object belongs to from a few examples.
Papers >Attention Mechanisms
Neural Machine Translation by Jointly Learning to Align and Translate
Attention mechanism for LSTMs! Mostly, figures and formulas and their explanations revealed to be useful to me. I gave a talk on that paper here.
Neural Turing Machines
Outstanding for letting a neural network learn an algorithm with seemingly good generalization over long time dependencies. Sequences recall problem.
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
LSTMs' attention mechanisms on CNNs feature maps does wonders.
Teaching Machines to Read and Comprehend
A very interesting and creative work about textual question answering, what a breakthrough, there is something to do with that.
Effective Approaches to Attention-based Neural Machine Translation
Exploring different approaches to attention mechanisms.
Matching Networks for One Shot Learning
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.
Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
In 2016: stacked residual LSTMs with attention mechanisms on encoder/decoder are the best for NMT (Neural Machine Translation).
Hybrid computing using a neural network with dynamic external memory
Improvements on differentiable memory based on NTMs: now it is the Differentiable Neural Computer (DNC).
Massive Exploration of Neural Machine Translation Architectures
That yields intuition about the boundaries of what works for doing NMT within a framed seq2seq problem formulation.
Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions
A WaveNet 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.
Attention Is All You Need
(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 this explanation and this visualization of the paper).
In 6 listsDetails
Papers >Other
ProjectionNet: Learning Efficient On-Device Deep Networks Using Neural Projections
Replace word embeddings by word projections in your deep neural networks, which doesn't require a pre-extracted dictionnary nor storing embedding matrices.
Self-Governing Neural Networks for On-Device Short Text Classification
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 attempt to reproduce the paper in code and watch the talks' recording).
Matching Networks for One Shot Learning
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…
YouTube and Videos
Attention Mechanisms in Recurrent Neural Networks (RNNs) - IGGG
A talk for a reading group on attention mechanisms (Paper: Neural Machine Translation by Jointly Learning to Align and Translate).
Tensor Calculus and the Calculus of Moving Surfaces
Generalize properly how Tensors work, yet just watching a few videos already helps a lot to grasp the concepts.
Deep Learning & Machine Learning (Advanced topics)
A list of videos about deep learning that I found interesting or useful, this is a mix of a bit of everything.
Signal Processing Playlist
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).
Computer Science
Yet another YouTube playlist I composed, this time about various CS topics.
Siraj's Channel
Siraj has entertaining, fast-paced video tutorials about deep learning.
Two Minute Papers' Channel
Interesting and shallow overview of some research papers, for example about WaveNet or Neural Style Transfer.
Geoffrey Hinton interview
Andrew Ng interviews Geoffrey Hinton, who talks about his research and breaktroughs, and gives advice for students.
Growing Neat Software Architecture from Jupyter Notebooks
A primer on how to structure your Machine Learning projects when using Jupyter Notebooks.
Misc. Hubs & Links
Hacker News
Maybe how I discovered ML - Interesting trends appear on that site way before they get to be a big deal.
In 3 lists
DataTau
This is a hub similar to Hacker News, but specific to data science.
Naver
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 Sanity Preserver
arXiv browser with TF/IDF features.
In 4 lists
Awesome Neuraxle
An awesome list for Neuraxle, a ML Framework for coding clean production-level ML pipelines.
Related lists in Computer Science
See category94
Table of Contents
hesreallyhim/awesome-claude-code
A hand-picked collection of the finest of resources for the most awesome of agents, Claude Code, the undisputed champion of coding companions, from the unstoppable team…
94
Awesome Agent Skills
VoltAgent/awesome-agent-skills
A curated collection of 1000+ agent skills from official dev teams and the community, compatible with Claude Code, Codex, Gemini CLI, Cursor, and more.
93
Awesome Machine Learning
josephmisiti/awesome-machine-learning
A curated list of awesome Machine Learning frameworks, libraries and software.
92
Awesome Production Machine Learning
EthicalML/awesome-production-machine-learning
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
92
AWESOME DATA SCIENCE
academic/awesome-datascience
:memo: An awesome Data Science repository to learn and apply for real world problems.
91
Static Analysis
analysis-tools-dev/static-analysis
⚙️ A curated list of static analysis (SAST) tools and linters for all programming languages, config files, build tools, and more. The focus is on tools which improve…