Awesome Deep Learning
Table of Contents
Books
- Deep Learning by Yoshua Bengio, Ian Goodfellow and Aaron Courville (05/07/2015)
- Neural Networks and Deep Learning by Michael Nielsen (Dec 2014)
- Deep Learning by Microsoft Research (2013)
- Deep Learning Tutorial by LISA lab, University of Montreal (Jan 6 2015)
- neuraltalk by Andrej Karpathy : numpy-based RNN/LSTM implementation
- An introduction to genetic algorithms
- Artificial Intelligence: A Modern Approach
- Deep Learning in Neural Networks: An Overview
- Artificial intelligence and machine learning: Topic wise explanation
- Grokking Deep Learning for Computer Vision
- Dive into Deep Learning - numpy based interactive Deep Learning book
- Practical Deep Learning for Cloud, Mobile, and Edge - A book for optimization techniques during production.
- Math and Architectures of Deep Learning - by Krishnendu Chaudhury
- TensorFlow 2.0 in Action - by Thushan Ganegedara
- Deep Learning for Natural Language Processing - by Stephan Raaijmakers
- Deep Learning Patterns and Practices - by Andrew Ferlitsch
- Inside Deep Learning - by Edward Raff
- Deep Learning with Python, Second Edition - by François Chollet
- Evolutionary Deep Learning - by Micheal Lanham
- Engineering Deep Learning Platforms - by Chi Wang and Donald Szeto
- Deep Learning with R, Second Edition - by François Chollet with Tomasz Kalinowski and J. J. Allaire
- Regularization in Deep Learning - by Liu Peng
- Jax in Action - by Grigory Sapunov
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron | Oct 15, 2019
Courses
- Machine Learning - Stanford by Andrew Ng in Coursera (2010-2014)
- Machine Learning - Caltech by Yaser Abu-Mostafa (2012-2014)
- Machine Learning - Carnegie Mellon by Tom Mitchell (Spring 2011)
- Neural Networks for Machine Learning by Geoffrey Hinton in Coursera (2012)
- Neural networks class by Hugo Larochelle from Université de Sherbrooke (2013)
- Deep Learning Course by CILVR lab @ NYU (2014)
- A.I - Berkeley by Dan Klein and Pieter Abbeel (2013)
- A.I - MIT by Patrick Henry Winston (2010)
- Vision and learning - computers and brains by Shimon Ullman, Tomaso Poggio, Ethan Meyers @ MIT (2013)
- Convolutional Neural Networks for Visual Recognition - Stanford by Fei-Fei Li, Andrej Karpathy (2017)
- Deep Learning for Natural Language Processing - Stanford
- Neural Networks - usherbrooke
- Machine Learning - Oxford (2014-2015)
- Deep Learning - Nvidia (2015)
- Graduate Summer School: Deep Learning, Feature Learning by Geoffrey Hinton, Yoshua Bengio, Yann LeCun, Andrew Ng, Nando de Freitas and several others @ IPAM, UCLA (2012)
- Deep Learning - Udacity/Google by Vincent Vanhoucke and Arpan Chakraborty (2016)
- Deep Learning - UWaterloo by Prof. Ali Ghodsi at University of Waterloo (2015)
- Statistical Machine Learning - CMU by Prof. Larry Wasserman
- Deep Learning Course by Yann LeCun (2016)
- Designing, Visualizing and Understanding Deep Neural Networks-UC Berkeley
- UVA Deep Learning Course MSc in Artificial Intelligence for the University of Amsterdam.
- MIT 6.S094: Deep Learning for Self-Driving Cars
- MIT 6.S191: Introduction to Deep Learning
- Berkeley CS 294: Deep Reinforcement Learning
- Keras in Motion video course
- Practical Deep Learning For Coders by Jeremy Howard - Fast.ai
- Introduction to Deep Learning by Prof. Bhiksha Raj (2017)
- AI for Everyone by Andrew Ng (2019)
- MIT Intro to Deep Learning 7 day bootcamp - A seven day bootcamp designed in MIT to introduce deep learning methods and applications (2019)
- Deep Blueberry: Deep Learning - A free five-weekend plan to self-learners to learn the basics of deep-learning architectures like CNNs, LSTMs, RNNs, VAEs, GANs, DQN, A3C and more (2019)
- Spinning Up in Deep Reinforcement Learning - A free deep reinforcement learning course by OpenAI (2019)
- Deep Learning Specialization - Coursera - Breaking into AI with the best course from Andrew NG.
- Deep Learning - UC Berkeley | STAT-157 by Alex Smola and Mu Li (2019)
- Machine Learning for Mere Mortals video course by Nick Chase
- Machine Learning Crash Course with TensorFlow APIs -Google AI
- Deep Learning from the Foundations Jeremy Howard - Fast.ai
- Deep Reinforcement Learning (nanodegree) - Udacity a 3-6 month Udacity nanodegree, spanning multiple courses (2018)
- Grokking Deep Learning in Motion by Beau Carnes (2018)
- Face Detection with Computer Vision and Deep Learning by Hakan Cebeci
- Deep Learning Online Course list at Classpert List of Deep Learning online courses (some are free) from Classpert Online Course Search
- AWS Machine Learning Machine Learning and Deep Learning Courses from Amazon's Machine Learning university
- Intro to Deep Learning with PyTorch - A great introductory course on Deep Learning by Udacity and Facebook AI
- Deep Learning by Kaggle - Kaggle's free course on Deep Learning
- Yann LeCun’s Deep Learning Course at CDS - DS-GA 1008 · SPRING 2021
- Neural Networks and Deep Learning - COMP9444 19T3
- Deep Learning A.I.Shelf
Videos and Lectures
- How To Create A Mind By Ray Kurzweil
- Deep Learning, Self-Taught Learning and Unsupervised Feature Learning By Andrew Ng
- Recent Developments in Deep Learning By Geoff Hinton
- The Unreasonable Effectiveness of Deep Learning by Yann LeCun
- Deep Learning of Representations by Yoshua bengio
- Principles of Hierarchical Temporal Memory by Jeff Hawkins
- Machine Learning Discussion Group - Deep Learning w/ Stanford AI Lab by Adam Coates
- Making Sense of the World with Deep Learning By Adam Coates
- Demystifying Unsupervised Feature Learning By Adam Coates
- Visual Perception with Deep Learning By Yann LeCun
- The Next Generation of Neural Networks By Geoffrey Hinton at GoogleTechTalks
- The wonderful and terrifying implications of computers that can learn By Jeremy Howard at TEDxBrussels
- Unsupervised Deep Learning - Stanford by Andrew Ng in Stanford (2011)
- Natural Language Processing By Chris Manning in Stanford
- A beginners Guide to Deep Neural Networks By Natalie Hammel and Lorraine Yurshansky
- Deep Learning: Intelligence from Big Data by Steve Jurvetson (and panel) at VLAB in Stanford.
- Introduction to Artificial Neural Networks and Deep Learning by Leo Isikdogan at Motorola Mobility HQ
- NIPS 2016 lecture and workshop videos - NIPS 2016
- Deep Learning Crash Course: a series of mini-lectures by Leo Isikdogan on YouTube (2018)
- Deep Learning Crash Course By Oliver Zeigermann
- Deep Learning with R in Motion: a live video course that teaches how to apply deep learning to text and images using the powerful Keras library and its R language interface.
- Medical Imaging with Deep Learning Tutorial: This tutorial is styled as a graduate lecture about medical imaging with deep learning. This will cover the background of popular medical image domains (chest X-ray and histology) as well as methods to tackle multi-modality/view, segmentation, and counting tasks.
- Deepmind x UCL Deeplearning: 2020 version
- Deepmind x UCL Reinforcement Learning: Deep Reinforcement Learning
- CMU 11-785 Intro to Deep learning Spring 2020 Course: 11-785, Intro to Deep Learning by Bhiksha Raj
- Machine Learning CS 229 : End part focuses on deep learning By Andrew Ng
- What is Neural Structured Learning by Andrew Ferlitsch
- Deep Learning Design Patterns by Andrew Ferlitsch
- Architecture of a Modern CNN: the design pattern approach by Andrew Ferlitsch
- Metaparameters in a CNN by Andrew Ferlitsch
- Multi-task CNN: a real-world example by Andrew Ferlitsch
- A friendly introduction to deep reinforcement learning by Luis Serrano
- What are GANs and how do they work? by Edward Raff
- Coding a basic WGAN in PyTorch by Edward Raff
- Training a Reinforcement Learning Agent by Miguel Morales
- Understand what is Deep Learning
Papers
You can also find the most cited deep learning papers from here
- ImageNet Classification with Deep Convolutional Neural Networks
- Using Very Deep Autoencoders for Content Based Image Retrieval
- Learning Deep Architectures for AI
- CMU’s list of papers
- Neural Networks for Named Entity Recognition zip
- Training tricks by YB
- Geoff Hinton's reading list (all papers)
- Supervised Sequence Labelling with Recurrent Neural Networks
- Statistical Language Models based on Neural Networks
- Training Recurrent Neural Networks
- Recursive Deep Learning for Natural Language Processing and Computer Vision
- Bi-directional RNN
- LSTM
- GRU - Gated Recurrent Unit
- GFRNN . .
- LSTM: A Search Space Odyssey
- A Critical Review of Recurrent Neural Networks for Sequence Learning
- Visualizing and Understanding Recurrent Networks
- Wojciech Zaremba, Ilya Sutskever, An Empirical Exploration of Recurrent Network Architectures
- Recurrent Neural Network based Language Model
- Extensions of Recurrent Neural Network Language Model
- Recurrent Neural Network based Language Modeling in Meeting Recognition
- Deep Neural Networks for Acoustic Modeling in Speech Recognition
- Speech Recognition with Deep Recurrent Neural Networks
- Reinforcement Learning Neural Turing Machines
- Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
- Google - Sequence to Sequence Learning with Neural Networks
- Memory Networks
- Policy Learning with Continuous Memory States for Partially Observed Robotic Control
- Microsoft - Jointly Modeling Embedding and Translation to Bridge Video and Language
- Neural Turing Machines
- Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
- Mastering the Game of Go with Deep Neural Networks and Tree Search
- Batch Normalization
- Residual Learning
- Image-to-Image Translation with Conditional Adversarial Networks
- Berkeley AI Research (BAIR) Laboratory
- MobileNets by Google
- Cross Audio-Visual Recognition in the Wild Using Deep Learning
- Dynamic Routing Between Capsules
- Matrix Capsules With Em Routing
- Efficient BackProp
- Generative Adversarial Nets
- Fast R-CNN
- FaceNet: A Unified Embedding for Face Recognition and Clustering
- Siamese Neural Networks for One-shot Image Recognition
- Unsupervised Translation of Programming Languages
- Matching Networks for One Shot Learning
- VOLO: Vision Outlooker for Visual Recognition
- ViT: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- DeepFaceDrawing: Deep Generation of Face Images from Sketches
Tutorials
- UFLDL Tutorial 1
- UFLDL Tutorial 2
- Deep Learning for NLP (without Magic)
- A Deep Learning Tutorial: From Perceptrons to Deep Networks
- Deep Learning from the Bottom up
- Theano Tutorial
- Neural Networks for Matlab
- Using convolutional neural nets to detect facial keypoints tutorial
- Torch7 Tutorials
- The Best Machine Learning Tutorials On The Web
- VGG Convolutional Neural Networks Practical
- TensorFlow tutorials
- More TensorFlow tutorials
- TensorFlow Python Notebooks
- Keras and Lasagne Deep Learning Tutorials
- Classification on raw time series in TensorFlow with a LSTM RNN
- Using convolutional neural nets to detect facial keypoints tutorial
- TensorFlow-World
- Deep Learning with Python
- Grokking Deep Learning
- Deep Learning for Search
- Keras Tutorial: Content Based Image Retrieval Using a Convolutional Denoising Autoencoder
- Pytorch Tutorial by Yunjey Choi
- Understanding deep Convolutional Neural Networks with a practical use-case in Tensorflow and Keras
- Overview and benchmark of traditional and deep learning models in text classification
- Hardware for AI: Understanding computer hardware & build your own computer
- Programming Community Curated Resources
- The Illustrated Self-Supervised Learning
- Visual Paper Summary: ALBERT (A Lite BERT)
- Semi-Supervised Deep Learning with GANs for Melanoma Detection
- Named Entity Recognition using Reformers
- Deep N-Gram Models on Shakespeare’s works
- Wide Residual Networks
- Fashion MNIST using Flax
- Fake News Classification (with streamlit deployment)
- Regression Analysis for Primary Biliary Cirrhosis
- Cross Matching Methods for Astronomical Catalogs
- Named Entity Recognition using BiDirectional LSTMs
- Image Recognition App using Tflite and Flutter
Researchers
- Aaron Courville
- Abdel-rahman Mohamed
- Adam Coates
- Alex Acero
- Alex Krizhevsky
- Alexander Ilin
- Amos Storkey
- Andrej Karpathy
- Andrew M. Saxe
- Andrew Ng
- Andrew W. Senior
- Andriy Mnih
- Ayse Naz Erkan
- Benjamin Schrauwen
- Bernardete Ribeiro
- Bo David Chen
- Boureau Y-Lan
- Brian Kingsbury
- Christopher Manning
- Clement Farabet
- Dan Claudiu Cireșan
- David Reichert
- Derek Rose
- Dong Yu
- [ Drausin