# Deep Learning Books, Tutorials, and Courses

## Free Course in Deep Learning

* [Coursera: Reinforcement Learning](https://www.coursera.org/specializations/reinforcement-learning)
* [Yann Le Cun Pytorch Deep Learning](https://atcold.github.io/pytorch-Deep-Learning/), [Youtube](https://www.youtube.com/playlist?list=PLLHTzKZzVU9eaEyErdV26ikyolxOsz6mq), [Github](https://github.com/Atcold/pytorch-Deep-Learning)
* [Python ML Course](https://github.com/leriomaggio/python-ml-course) License: MIT
* [TensorFlow, Keras and deep learning, without a PhD](https://codelabs.developers.google.com/codelabs/cloud-tensorflow-mnist#0), [Github](https://github.com/GoogleCloudPlatform/tensorflow-without-a-phd) License: Apache
* [Deep Learning with Tensorflow](https://github.com/Rishit-dagli/Deep-Learning-With-TensorFlow) License: Apache
* [mrdbourke/tensorflow-deep-learning: All course materials for the Zero to Mastery Deep Learning with TensorFlow course.](https://github.com/mrdbourke/tensorflow-deep-learning) License: MIT

## Open Licensed Deep Learning Books and Tutorials

* [Deep Learning with Keras and Tensorflow](https://github.com/leriomaggio/deep-learning-keras-tensorflow) License: MIT
* [Practical Deep Learning for Coders](https://course.fast.ai/) [GitHub](https://github.com/fastai/course-v3) License: Apache
* [Dive into Deep Learning](https://d2l.ai/), [Github](https://github.com/d2l-ai/d2l-en) License: CC-BY
* [Spinning Up in Deep RL](https://spinningup.openai.com/en/latest/user/introduction.html), [GitHub](https://github.com/openai/spinningup) License: MIT

## CC-BY-NC Deep Learning Books and Tutorials

* [Neural Networks and Deep Learning](https://neuralnetworksanddeeplearning.com/) [Code at Github](https://github.com/mnielsen/neural-networks-and-deep-learning) License: CC-BY-NC

## Free Deep Learning Books and Tutorials

* [Deep Learning with PyTorch](https://www.manning.com/books/deep-learning-with-pytorch)
* [Deep Learning Project](https://spandan-madan.github.io/DeepLearningProject/) License: -
* [Code for Deep Learning Book](https://github.com/rasbt/deep-learning-book)
* [Samuel Sena](https://medium.com/@samuelsena/pengenalan-deep-learning-8fbb7d8028ac) : Deep Learning Tutorial in Indonesian

## List of Deep Learning Books

* [List Data Science Deep Learning Python](https://www.theinsaneapp.com/2020/08/free-data-science-deep-learning-python-ebooks.html)

## Deep Learning Tutorial

* [Dataflowr - Deep Learning DIY](https://dataflowr.github.io/website/)

## Visualization

* [ConvNetJS: Deep Learning in your browser](https://cs.stanford.edu/people/karpathy/convnetjs/)
* [A Neural Network Playground](http://playground.tensorflow.org/)

## eBook

* [Introduction - The Mathematical Engineering of Deep Learning](https://deeplearningmath.org/)
* [Full Stack Deep Learning - Full Stack Deep Learning](https://fall2019.fullstackdeeplearning.com/)
* [Dive into Deep Learning—Dive into Deep Learning 0.16.1 documentation](http://d2l.ai/index.html)

## Machine Learning

* [Machine Learning Crash Course - Google Developers](https://developers.google.com/machine-learning/crash-course)
* [Courses List Google Digital Garage](https://learndigital.withgoogle.com/digitalgarage/courses)
* [Notes on machine learning](https://peterroelants.github.io/)
* [karpathy/convnetjs: Deep Learning in Javascript. Train Convolutional Neural Networks (or ordinary ones) in your browser.](https://github.com/karpathy/convnetjs) Deep Learning in Javascript. Train Convolutional Neural Networks (or ordinary ones) in your browser.
* [MIT Deep Learning 6.S191](http://introtodeeplearning.com/)
* [Demystifying Deep Learning Primer](https://mukulrathi.co.uk/demystifying-deep-learning/maths-behind-deep-learning/)
* [FrancescoSaverioZuppichini/glasses: High-quality Neural Networks for Computer Vision 😎](https://github.com/FrancescoSaverioZuppichini/glasses)
* [Ground AI](https://www.groundai.com/) Our mission is to increase AI scholarly communication
* [khanhnamle1994/applied-machine-learning: A step-by-step guide to get started with Applied Machine Learning](https://github.com/khanhnamle1994/applied-machine-learning)
* [google/applied-machine-learning-intensive: Applied Machine Learning Intensive](https://github.com/google/applied-machine-learning-intensive) Bangkit
* [tfolkman/byu\_econ\_applied\_machine\_learning: The course work for the applied machine learning course I am teaching at BYU](https://github.com/tfolkman/byu_econ_applied_machine_learning)
* [amitkaps/applied-machine-learning: Applied Machine Learning @ http://amitkaps.com/ml](https://github.com/amitkaps/applied-machine-learning)

### Machine Learning Course

* [COMS W4995 Applied Machine Learning Spring 2019 - Schedule - Andreas C. Müller - Associate Research Scientist](https://www.cs.columbia.edu/~amueller/comsw4995s19/schedule/), [amueller/COMS4995-s19: COMS W4995 Applied Machine Learning - Spring 19](https://github.com/amueller/COMS4995-s19) License: CC0

### Learning NN

* [Autotelic Computing: Neural Network 101](http://cjauvin.blogspot.com/2013/10/neural-network-101.html)
* [Everything you need to know about Neural Networks and Backpropagation—Machine Learning Easy and Fun - by Gavril Ognjanovski - Towards Data Science](https://towardsdatascience.com/everything-you-need-to-know-about-neural-networks-and-backpropagation-machine-learning-made-easy-e5285bc2be3a)
* [Δ ℚuantitative √ourney - Gradient Descent with Backpropagation](http://outlace.com/Gradient-Descent.html)
* [Neural Networks: Feedforward and Backpropagation Explained](https://mlfromscratch.com/neural-networks-explained/#/)
* [How to Train Neural Networks With Backpropagation « The blog at the bottom of the sea](https://blog.demofox.org/2017/03/09/how-to-train-neural-networks-with-backpropagation/)
* [Rohan & Lenny Neural Networks & The Backpropagation Algorithm, Explained - by Rohan Kapur - A Year of Artificial Intelligence](https://ayearofai.com/rohan-lenny-1-neural-networks-the-backpropagation-algorithm-explained-abf4609d4f9d#.7mwcjuftn)
* [Backpropagation Tutorial - Manfred Zabarauskas' Blog](http://blog.zabarauskas.com/backpropagation-tutorial/)
* [A Visual Tour of Backpropagation](https://blog.jinay.dev/posts/backprop/)

### Machine Learning

* [Machine Learning for Everyone](https://vas3k.com/blog/machine_learning/)
* [Vas3k](https://vas3k.com/) : good blog
* [Neural Network Zoo](https://www.asimovinstitute.org/neural-network-zoo/)
* [GPU Benchmarks for Deep Learning - Lambda](https://lambdalabs.com/gpu-benchmarks)
* [Introduction to Computer Science and Programming Using Python - edX](https://www.edx.org/course/introduction-to-computer-science-and-programming-7)
* [DALL·E: Creating Images from Text](https://openai.com/blog/dall-e/)
* [CLIP: Connecting Text and Images](https://openai.com/blog/clip/)
* [lucko515/cnn-raccoon: Create interactive dashboards for your Convolutional Neural Networks with a single line of code!](https://github.com/lucko515/cnn-raccoon)
* [Ecco - Look Inside Language Models](https://www.eccox.io/)

## Deep Learning References

### Books

* [Deep Learning, Ian Goodfellow and others](http://www.deeplearningbook.org/)
* [Neural Networks and Deep Learning, Michael Nielsen](http://neuralnetworksanddeeplearning.com/)

### Courses

* [cs231n.github.io](http://cs231n.github.io/)
* [Berkeley deep RL course](http://rll.berkeley.edu/deeprlcourse/)
* [CS 598 LAZ](https://slazebni.cs.illinois.edu/spring17/)

### Guides to deep learning

* [A guide to deep learning by YerevaNN research labs](http://yerevann.com/a-guide-to-deep-learning/)
* [Unsupervised feature learning and deep learning tutorial](http://ufldl.stanford.edu/tutorial/), [amaas/stanford\_dl\_ex: Programming exercises for the Stanford Unsupervised Feature Learning and Deep Learning Tutorial](https://github.com/amaas/stanford_dl_ex)
* [Most cited deep learning papers](https://github.com/terryum/awesome-deep-learning-papers)

### Tutorials, blogs, demos

* Convolution arithmetic tutorial: [web](http://deeplearning.net/software/theano_versions/dev/tutorial/conv_arithmetic.html), [arXiv](https://arxiv.org/abs/1603.07285)
* [TensorFlow Playground](http://playground.tensorflow.org/)
* <http://distill.pub/>
* <http://karpathy.github.io/>
* [Arun's dilation demo](http://jsfiddle.net/yces4vn9/43/)
* [Arun's explanation of LSTM Forward-Backward passes](http://arunmallya.github.io/writeups/nn/lstm/index.html#/)
* [Mixture Density Network tensorflow](http://blog.otoro.net/2015/11/24/mixture-density-networks-with-tensorflow/)

## Deep Learning

* [DEEP LEARNING · Deep Learning](https://atcold.github.io/NYU-DLSP21/)

## Machine Learning: Blog

* [Rahmadya Trias Handayanto - "Just for a little kindness"](https://rahmadya.com/)


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