awesome-deep-learning

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# Awesome Deep Learning Awesome

Table of Contents

Books

  1. Deep Learning by Yoshua Bengio, Ian Goodfellow and Aaron Courville (05/07/2015)
  2. Neural Networks and Deep Learning by Michael Nielsen (Dec 2014)
  3. Deep Learning by Microsoft Research (2013)
  4. Deep Learning Tutorial by LISA lab, University of Montreal (Jan 6 2015)
  5. neuraltalk by Andrej Karpathy : numpy-based RNN/LSTM implementation
  6. An introduction to genetic algorithms
  7. Artificial Intelligence: A Modern Approach
  8. Deep Learning in Neural Networks: An Overview
  9. Artificial intelligence and machine learning: Topic wise explanation
  10. Grokking Deep Learning for Computer Vision
  11. Dive into Deep Learning - numpy based interactive Deep Learning book
  12. Practical Deep Learning for Cloud, Mobile, and Edge - A book for optimization techniques during production.
  13. Math and Architectures of Deep Learning - by Krishnendu Chaudhury
  14. TensorFlow 2.0 in Action - by Thushan Ganegedara
  15. Deep Learning for Natural Language Processing - by Stephan Raaijmakers
  16. Deep Learning Patterns and Practices - by Andrew Ferlitsch
  17. Inside Deep Learning - by Edward Raff
  18. Deep Learning with Python, Second Edition - by François Chollet
  19. Evolutionary Deep Learning - by Micheal Lanham
  20. Engineering Deep Learning Platforms - by Chi Wang and Donald Szeto
  21. Deep Learning with R, Second Edition - by François Chollet with Tomasz Kalinowski and J. J. Allaire
  22. Regularization in Deep Learning - by Liu Peng
  23. Jax in Action - by Grigory Sapunov
  24. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron | Oct 15, 2019

Courses

  1. Machine Learning - Stanford by Andrew Ng in Coursera (2010-2014)
  2. Machine Learning - Caltech by Yaser Abu-Mostafa (2012-2014)
  3. Machine Learning - Carnegie Mellon by Tom Mitchell (Spring 2011)
  4. Neural Networks for Machine Learning by Geoffrey Hinton in Coursera (2012)
  5. Neural networks class by Hugo Larochelle from Université de Sherbrooke (2013)
  6. Deep Learning Course by CILVR lab @ NYU (2014)
  7. A.I - Berkeley by Dan Klein and Pieter Abbeel (2013)
  8. A.I - MIT by Patrick Henry Winston (2010)
  9. Vision and learning - computers and brains by Shimon Ullman, Tomaso Poggio, Ethan Meyers @ MIT (2013)
  10. Convolutional Neural Networks for Visual Recognition - Stanford by Fei-Fei Li, Andrej Karpathy (2017)
  11. Deep Learning for Natural Language Processing - Stanford
  12. Neural Networks - usherbrooke
  13. Machine Learning - Oxford (2014-2015)
  14. Deep Learning - Nvidia (2015)
  15. 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)
  16. Deep Learning - Udacity/Google by Vincent Vanhoucke and Arpan Chakraborty (2016)
  17. Deep Learning - UWaterloo by Prof. Ali Ghodsi at University of Waterloo (2015)
  18. Statistical Machine Learning - CMU by Prof. Larry Wasserman
  19. Deep Learning Course by Yann LeCun (2016)
  20. Designing, Visualizing and Understanding Deep Neural Networks-UC Berkeley
  21. UVA Deep Learning Course MSc in Artificial Intelligence for the University of Amsterdam.
  22. MIT 6.S094: Deep Learning for Self-Driving Cars
  23. MIT 6.S191: Introduction to Deep Learning
  24. Berkeley CS 294: Deep Reinforcement Learning
  25. Keras in Motion video course
  26. Practical Deep Learning For Coders by Jeremy Howard - Fast.ai
  27. Introduction to Deep Learning by Prof. Bhiksha Raj (2017)
  28. AI for Everyone by Andrew Ng (2019)
  29. MIT Intro to Deep Learning 7 day bootcamp - A seven day bootcamp designed in MIT to introduce deep learning methods and applications (2019)
  30. 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)
  31. Spinning Up in Deep Reinforcement Learning - A free deep reinforcement learning course by OpenAI (2019)
  32. Deep Learning Specialization - Coursera - Breaking into AI with the best course from Andrew NG.
  33. Deep Learning - UC Berkeley | STAT-157 by Alex Smola and Mu Li (2019)
  34. Machine Learning for Mere Mortals video course by Nick Chase
  35. Machine Learning Crash Course with TensorFlow APIs -Google AI
  36. Deep Learning from the Foundations Jeremy Howard - Fast.ai
  37. Deep Reinforcement Learning (nanodegree) - Udacity a 3-6 month Udacity nanodegree, spanning multiple courses (2018)
  38. Grokking Deep Learning in Motion by Beau Carnes (2018)
  39. Face Detection with Computer Vision and Deep Learning by Hakan Cebeci
  40. Deep Learning Online Course list at Classpert List of Deep Learning online courses (some are free) from Classpert Online Course Search
  41. AWS Machine Learning Machine Learning and Deep Learning Courses from Amazon's Machine Learning university
  42. Intro to Deep Learning with PyTorch - A great introductory course on Deep Learning by Udacity and Facebook AI
  43. Deep Learning by Kaggle - Kaggle's free course on Deep Learning
  44. Yann LeCun’s Deep Learning Course at CDS - DS-GA 1008 · SPRING 2021
  45. Neural Networks and Deep Learning - COMP9444 19T3
  46. Deep Learning A.I.Shelf

Videos and Lectures

  1. How To Create A Mind By Ray Kurzweil
  2. Deep Learning, Self-Taught Learning and Unsupervised Feature Learning By Andrew Ng
  3. Recent Developments in Deep Learning By Geoff Hinton
  4. The Unreasonable Effectiveness of Deep Learning by Yann LeCun
  5. Deep Learning of Representations by Yoshua bengio
  6. Principles of Hierarchical Temporal Memory by Jeff Hawkins
  7. Machine Learning Discussion Group - Deep Learning w/ Stanford AI Lab by Adam Coates
  8. Making Sense of the World with Deep Learning By Adam Coates
  9. Demystifying Unsupervised Feature Learning By Adam Coates
  10. Visual Perception with Deep Learning By Yann LeCun
  11. The Next Generation of Neural Networks By Geoffrey Hinton at GoogleTechTalks
  12. The wonderful and terrifying implications of computers that can learn By Jeremy Howard at TEDxBrussels
  13. Unsupervised Deep Learning - Stanford by Andrew Ng in Stanford (2011)
  14. Natural Language Processing By Chris Manning in Stanford
  15. A beginners Guide to Deep Neural Networks By Natalie Hammel and Lorraine Yurshansky
  16. Deep Learning: Intelligence from Big Data by Steve Jurvetson (and panel) at VLAB in Stanford.
  17. Introduction to Artificial Neural Networks and Deep Learning by Leo Isikdogan at Motorola Mobility HQ
  18. NIPS 2016 lecture and workshop videos - NIPS 2016
  19. Deep Learning Crash Course: a series of mini-lectures by Leo Isikdogan on YouTube (2018)
  20. Deep Learning Crash Course By Oliver Zeigermann
  21. 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.
  22. 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.
  23. Deepmind x UCL Deeplearning: 2020 version
  24. Deepmind x UCL Reinforcement Learning: Deep Reinforcement Learning
  25. CMU 11-785 Intro to Deep learning Spring 2020 Course: 11-785, Intro to Deep Learning by Bhiksha Raj
  26. Machine Learning CS 229 : End part focuses on deep learning By Andrew Ng
  27. What is Neural Structured Learning by Andrew Ferlitsch
  28. Deep Learning Design Patterns by Andrew Ferlitsch
  29. Architecture of a Modern CNN: the design pattern approach by Andrew Ferlitsch
  30. Metaparameters in a CNN by Andrew Ferlitsch
  31. Multi-task CNN: a real-world example by Andrew Ferlitsch
  32. A friendly introduction to deep reinforcement learning by Luis Serrano
  33. What are GANs and how do they work? by Edward Raff
  34. Coding a basic WGAN in PyTorch by Edward Raff
  35. Training a Reinforcement Learning Agent by Miguel Morales
  36. Understand what is Deep Learning

Papers

You can also find the most cited deep learning papers from here

  1. ImageNet Classification with Deep Convolutional Neural Networks
  2. Using Very Deep Autoencoders for Content Based Image Retrieval
  3. Learning Deep Architectures for AI
  4. CMU’s list of papers
  5. Neural Networks for Named Entity Recognition zip
  6. Training tricks by YB
  7. Geoff Hinton's reading list (all papers)
  8. Supervised Sequence Labelling with Recurrent Neural Networks
  9. Statistical Language Models based on Neural Networks
  10. Training Recurrent Neural Networks
  11. Recursive Deep Learning for Natural Language Processing and Computer Vision
  12. Bi-directional RNN
  13. LSTM
  14. GRU - Gated Recurrent Unit
  15. GFRNN . .
  16. LSTM: A Search Space Odyssey
  17. A Critical Review of Recurrent Neural Networks for Sequence Learning
  18. Visualizing and Understanding Recurrent Networks
  19. Wojciech Zaremba, Ilya Sutskever, An Empirical Exploration of Recurrent Network Architectures
  20. Recurrent Neural Network based Language Model
  21. Extensions of Recurrent Neural Network Language Model
  22. Recurrent Neural Network based Language Modeling in Meeting Recognition
  23. Deep Neural Networks for Acoustic Modeling in Speech Recognition
  24. Speech Recognition with Deep Recurrent Neural Networks
  25. Reinforcement Learning Neural Turing Machines
  26. Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
  27. Google - Sequence to Sequence Learning with Neural Networks
  28. Memory Networks
  29. Policy Learning with Continuous Memory States for Partially Observed Robotic Control
  30. Microsoft - Jointly Modeling Embedding and Translation to Bridge Video and Language
  31. Neural Turing Machines
  32. Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
  33. Mastering the Game of Go with Deep Neural Networks and Tree Search
  34. Batch Normalization
  35. Residual Learning
  36. Image-to-Image Translation with Conditional Adversarial Networks
  37. Berkeley AI Research (BAIR) Laboratory
  38. MobileNets by Google
  39. Cross Audio-Visual Recognition in the Wild Using Deep Learning
  40. Dynamic Routing Between Capsules
  41. Matrix Capsules With Em Routing
  42. Efficient BackProp
  43. Generative Adversarial Nets
  44. Fast R-CNN
  45. FaceNet: A Unified Embedding for Face Recognition and Clustering
  46. Siamese Neural Networks for One-shot Image Recognition
  47. Unsupervised Translation of Programming Languages
  48. Matching Networks for One Shot Learning
  49. VOLO: Vision Outlooker for Visual Recognition
  50. ViT: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
  51. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
  52. DeepFaceDrawing: Deep Generation of Face Images from Sketches

Tutorials

  1. UFLDL Tutorial 1
  2. UFLDL Tutorial 2
  3. Deep Learning for NLP (without Magic)
  4. A Deep Learning Tutorial: From Perceptrons to Deep Networks
  5. Deep Learning from the Bottom up
  6. Theano Tutorial
  7. Neural Networks for Matlab
  8. Using convolutional neural nets to detect facial keypoints tutorial
  9. Torch7 Tutorials
  10. The Best Machine Learning Tutorials On The Web
  11. VGG Convolutional Neural Networks Practical
  12. TensorFlow tutorials
  13. More TensorFlow tutorials
  14. TensorFlow Python Notebooks
  15. Keras and Lasagne Deep Learning Tutorials
  16. Classification on raw time series in TensorFlow with a LSTM RNN
  17. Using convolutional neural nets to detect facial keypoints tutorial
  18. TensorFlow-World
  19. Deep Learning with Python
  20. Grokking Deep Learning
  21. Deep Learning for Search
  22. Keras Tutorial: Content Based Image Retrieval Using a Convolutional Denoising Autoencoder
  23. Pytorch Tutorial by Yunjey Choi
  24. Understanding deep Convolutional Neural Networks with a practical use-case in Tensorflow and Keras
  25. Overview and benchmark of traditional and deep learning models in text classification
  26. Hardware for AI: Understanding computer hardware & build your own computer
  27. Programming Community Curated Resources
  28. The Illustrated Self-Supervised Learning
  29. Visual Paper Summary: ALBERT (A Lite BERT)
  30. Semi-Supervised Deep Learning with GANs for Melanoma Detection
  31. Named Entity Recognition using Reformers
  32. Deep N-Gram Models on Shakespeare’s works
  33. Wide Residual Networks
  34. Fashion MNIST using Flax
  35. Fake News Classification (with streamlit deployment)
  36. Regression Analysis for Primary Biliary Cirrhosis
  37. Cross Matching Methods for Astronomical Catalogs
  38. Named Entity Recognition using BiDirectional LSTMs
  39. Image Recognition App using Tflite and Flutter

README 内容较长，此处已截断，完整内容请查看 GitHub 仓库。

文档抓取自 GitHub 仓库 README，版权归原作者所有；已过滤徽章等噪音并经安全消毒后展示。