https://github.com/zackchase/mxnet-the-straight-dope

An interactive book on deep learning. Much easy, so MXNet. Wow. [Straight Dope is growing up] ---> Much of this content has been incorporated into the new Dive into Deep Learning Book available at https://d2l.ai/.
https://github.com/zackchase/mxnet-the-straight-dope

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An interactive book on deep learning. Much easy, so MXNet. Wow. [Straight Dope is growing up] ---> Much of this content has been incorporated into the new Dive into Deep Learning Book available at https://d2l.ai/.

• Host: GitHub
• URL: https://github.com/zackchase/mxnet-the-straight-dope
• Owner: zackchase
• License: apache-2.0
• Created: 2017-07-11T23:24:36.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2020-04-19T02:46:01.000Z (almost 5 years ago)
• Last Synced: 2025-01-25T07:03:06.243Z (7 days ago)
• Language: Jupyter Notebook
• Homepage: https://d2l.ai/
• Size: 210 MB
• Stars: 2,561
• Watchers: 137
• Forks: 723
• Open Issues: 124
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Deep Learning - The Straight Dope (*Deprecated* Please see d2l.ai)

## This content has been moved to Dive into the Deep Learning Book freely available at https://d2l.ai/.

## Abstract

This repo contains an

incremental sequence of notebooks designed to teach deep learning, MXNet, and

the ``gluon`` interface. Our goal is to leverage the strengths of Jupyter

notebooks to present prose, graphics, equations, and code together in one place.

If we're successful, the result will be a resource that could be simultaneously

a book, course material, a prop for live tutorials, and a resource for

plagiarising (with our blessing) useful code. To our knowledge there's no source

out there that teaches either (1) the full breadth of concepts in modern deep

learning or (2) interleaves an engaging textbook with runnable code. We'll find

out by the end of this venture whether or not that void exists for a good

reason.

Another unique aspect of this book is its authorship process. We are

developing this resource fully in the public view and are making it available

for free in its entirety. While the book has a few primary authors to set the

tone and shape the content, we welcome contributions from the community and hope

to coauthor chapters and entire sections with experts and community members.

Already we've received contributions spanning typo corrections through full

working examples.

## Implementation with Apache MXNet

Throughout this book,

we rely upon MXNet to teach core concepts, advanced topics, and a full

complement of applications. MXNet is widely used in production environments

owing to its strong reputation for speed. Now with ``gluon``, MXNet's new

imperative interface (alpha), doing research in MXNet is easy.

## Dependencies

To run these notebooks, you'll want to build MXNet from source. Fortunately,

this is easy (especially on Linux) if you follow [these

instructions](http://mxnet.io/get_started/install.html). You'll also want to

[install Jupyter](http://jupyter.readthedocs.io/en/latest/install.html) and use

Python 3 (because it's 2017).

## Slides

The authors (& others) are

increasingly giving talks that are based on the content in this books. Some of

these slide-decks (like the 6-hour KDD 2017) are gigantic so we're collecting

them separately in [this repo](https://github.com/zackchase/mxnet-slides).

Contribute there if you'd like to share tutorials or course material based on

this books.

## Translation

As we write the book, large stable sections are simultaneously being translated into 中文,

available in a [web version](http://zh.gluon.ai/) and via [GitHub source](http://zh.gluon.ai/).

## Table of contents

### Part 1: Deep Learning Fundamentals

* **Chapter 1:** Crash course

    * [Preface](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter01_crashcourse/preface.ipynb)

    * [Introduction](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter01_crashcourse/introduction.ipynb)

    * [Manipulating data with NDArray](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter01_crashcourse/ndarray.ipynb)

    * [Linear algebra](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter01_crashcourse/linear-algebra.ipynb)

    * [Probability and statistics](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter01_crashcourse/probability.ipynb)

    * [Automatic differentiation via ``autograd``](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter01_crashcourse/autograd.ipynb)

* **Chapter 2:** Introduction to supervised learning

    * [Linear regression *(from scratch)*](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter02_supervised-learning/linear-regression-scratch.ipynb)

    * [Linear regression *(with ``gluon``)*](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter02_supervised-learning/linear-regression-gluon.ipynb)

    * [Binary classification with logistic regression *(``gluon`` w bespoke loss function)*](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter02_supervised-learning/logistic-regression-gluon.ipynb)

    * [Multiclass logistic regression *(from scratch)*](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter02_supervised-learning/softmax-regression-scratch.ipynb)

    * [Multiclass logistic regression *(with ``gluon``)*](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter02_supervised-learning/softmax-regression-gluon.ipynb)

    * [Overfitting and regularization *(from scratch)*](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter02_supervised-learning/regularization-scratch.ipynb)

     * [Overfitting and regularization *(with ``gluon``)*](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter02_supervised-learning/regularization-gluon.ipynb)

     * [Perceptron and SGD primer](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter02_supervised-learning/perceptron.ipynb)

     * [Learning environments](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter02_supervised-learning/environment.ipynb)

* **Chapter 3:** Deep neural networks (DNNs)

    * [Multilayer perceptrons *(from scratch)*](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter03_deep-neural-networks/mlp-scratch.ipynb)

    * [Multilayer perceptrons *(with ``gluon``)*](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter03_deep-neural-networks/mlp-gluon.ipynb)

    * [Dropout regularization *(from scratch)*](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter03_deep-neural-networks/mlp-dropout-scratch.ipynb)

    * [Dropout regularization *(with ``gluon``)*](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter03_deep-neural-networks/mlp-dropout-gluon.ipynb)

    * [Introduction to ``gluon.Block`` and ``gluon.nn.Sequential()``](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter03_deep-neural-networks/plumbing.ipynb)

    * [Writing custom layers with ``gluon.Block``](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter03_deep-neural-networks/custom-layer.ipynb)

    * [Serialization: saving and loading models](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter03_deep-neural-networks/serialization.ipynb)

    * Advanced Data IO

    * Debugging your neural networks

* **Chapter 4:** Convolutional neural networks (CNNs)

     * [Convolutional neural networks *(from scratch)*](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter04_convolutional-neural-networks/cnn-scratch.ipynb)

     * [Convolutional neural networks *(with ``gluon``)*](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter04_convolutional-neural-networks/cnn-gluon.ipynb)

     * [Introduction to deep CNNs (AlexNet)](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter04_convolutional-neural-networks/deep-cnns-alexnet.ipynb)

     * [Very deep networks and repeating blocks (VGG network)](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter04_convolutional-neural-networks/very-deep-nets-vgg.ipynb)

     * [Batch normalization *(from scratch)*](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter04_convolutional-neural-networks/cnn-batch-norm-scratch.ipynb)

     * [Batch normalization *(with ``gluon``)*](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter04_convolutional-neural-networks/cnn-batch-norm-gluon.ipynb)

* **Chapter 5:** Recurrent neural networks (RNNs)

    * [Simple RNNs (from scratch)](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter05_recurrent-neural-networks/simple-rnn.ipynb)

    * [LSTMS RNNs (from scratch)](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter05_recurrent-neural-networks/lstm-scratch.ipynb)

    * [GRUs (from scratch)](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter05_recurrent-neural-networks/gru-scratch.ipynb)

    * [RNNs (with ``gluon``)](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter05_recurrent-neural-networks/rnns-gluon.ipynb)

    * ***Roadmap*** Dropout for recurrent nets

    * ***Roadmap*** Zoneout regularization

* **Chapter 6:** Optimization

    * [Introduction to optimization](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter06_optimization/optimization-intro.ipynb)

    * [Gradient descent and stochastic gradient descent from scratch](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter06_optimization/gd-sgd-scratch.ipynb)

    * [Gradient descent and stochastic gradient descent with `gluon`](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter06_optimization/gd-sgd-gluon.ipynb)

    * [Momentum from scratch](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter06_optimization/momentum-scratch.ipynb)

    * [Momentum with `gluon`](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter06_optimization/momentum-gluon.ipynb)

    * [Adagrad from scratch](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter06_optimization/adagrad-scratch.ipynb)

    * [Adagrad with `gluon`](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter06_optimization/adagrad-gluon.ipynb)

    * [RMSprop from scratch](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter06_optimization/rmsprop-scratch.ipynb)

    * [RMSprop with `gluon`](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter06_optimization/rmsprop-gluon.ipynb)

    * [Adadelta from scratch](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter06_optimization/adadelta-scratch.ipynb)

    * [Adadelta with `gluon`](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter06_optimization/adadelta-gluon.ipynb)

    * [Adam from scratch](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter06_optimization/adam-scratch.ipynb)

    * [Adam with `gluon`](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter06_optimization/adam-gluon.ipynb)

* **Chapter 7:** Distributed & high-performance learning

    * [Fast & flexible: combining imperative & symbolic nets with HybridBlocks](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter07_distributed-learning/hybridize.ipynb)

    * [Training with multiple GPUs (from scratch)](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter07_distributed-learning/multiple-gpus-scratch.ipynb)

    * [Training with multiple GPUs (with ``gluon``)](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter07_distributed-learning/multiple-gpus-gluon.ipynb)

    * [Training with multiple machines](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter07_distributed-learning/training-with-multiple-machines.ipynb)

    * ***Roadmap*** Asynchronous SGD

    * ***Roadmap*** Elastic SGD

### Part 2: Applications

* **Chapter 8:** Computer vision (CV)

    * ***Roadmap*** Network of networks (inception & co)

    * ***Roadmap*** Residual networks

    * [Object detection](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter08_computer-vision/object-detection.ipynb)

    * ***Roadmap*** Fully-convolutional networks

    * ***Roadmap*** Siamese (conjoined?) networks

    * ***Roadmap*** Embeddings (pairwise and triplet losses)

    * ***Roadmap*** Inceptionism / visualizing feature detectors

    * ***Roadmap*** Style transfer

    * [Visual-question-answer](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter08_computer-vision/visual-question-answer.ipynb)

    * [Fine-tuning](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter08_computer-vision/fine-tuning.ipynb)

* **Chapter 9:** Natural language processing (NLP)

    * ***Roadmap*** Word embeddings (Word2Vec)

    * ***Roadmap*** Sentence embeddings (SkipThought)

    * ***Roadmap*** Sentiment analysis

    * ***Roadmap*** Sequence-to-sequence learning (machine translation)

    * ***Roadmap*** Sequence transduction with attention (machine translation)

    * ***Roadmap*** Named entity recognition

    * ***Roadmap*** Image captioning

    * [Tree-LSTM for semantic relatedness](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter09_natural-language-processing/tree-lstm.ipynb)

* **Chapter 10:** Audio processing

    * ***Roadmap*** Intro to automatic speech recognition

    * ***Roadmap*** Connectionist temporal classification (CSC) for unaligned sequences

    * ***Roadmap*** Combining static and sequential data

* **Chapter 11:** Recommender systems

    * [Introduction to recommender systems](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter11_recommender-systems/intro-recommender-systems.ipynb)

    * ***Roadmap*** Latent factor models

    * ***Roadmap*** Deep latent factor models

    * ***Roadmap*** Bilinear models

    * ***Roadmap*** Learning from implicit feedback

* **Chapter 12:** Time series

    * [Introduction to Forecasting *(with ``gluon``)*](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter12_time-series/intro-forecasting-gluon.ipynb)

    * [Generalized Linear Models/MLP for Forecasting *(with ``gluon``)*](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter12_time-series/intro-forecasting-2-gluon.ipynb)

    * ***Roadmap*** Factor Models for Forecasting

    * ***Roadmap*** Recurrent Neural Network for Forecasting

    * [Linear Dynamical System (*from scratch*) ](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter12_time-series/lds-scratch.ipynb)

    * [Exponential Smoothing and Innovative State-space modeling (*from scratch*)](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter12_time-series/issm-scratch.ipynb)

    * ***Roadmap*** Gaussian processes for Forecasting

    * ***Roadmap*** Bayesian Time Series Models

    * ***Roadmap*** Modeling missing data

    * ***Roadmap*** Combining static and sequential data

### Part 3: Advanced Methods

* **Chapter 13:** Unsupervised learning

   * ***Roadmap*** Introduction to autoencoders

   * ***Roadmap*** Convolutional autoencoders (introduce upconvolution)

   * ***Roadmap*** Denoising autoencoders

   * [Variational autoencoders](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter13_unsupervised-learning/vae-gluon.ipynb)

   * ***Roadmap*** Clustering

* **Chapter 14:** Generative adversarial networks (GANs)

    * [Introduction to GANs](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter14_generative-adversarial-networks/gan-intro.ipynb)

    * [Deep convolutional GANs (DCGANs)](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter14_generative-adversarial-networks/dcgan.ipynb)

    * ***Roadmap*** Wasserstein-GANs

    * ***Roadmap*** Energy-based GANS

    * ***Roadmap*** Conditional GANs

    * [Image transduction GANs (Pix2Pix)](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter14_generative-adversarial-networks/pixel2pixel.ipynb)

    * ***Roadmap*** Learning from Synthetic and Unsupervised Images

* **Chapter 15:** Adversarial learning

    * ***Roadmap*** Two Sample Tests

    * ***Roadmap*** Finding adversarial examples

    * ***Roadmap*** Adversarial training

* **Chapter 16:** Tensor Methods

    * [Introduction to tensor methods](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter16_tensor_methods/tensor_basics.ipynb)

    * ***Roadmap*** Tensor decomposition

    * ***Roadmap*** Tensorized neural networks

* **Chapter 17:** Deep reinforcement learning (DRL)

    * ***Roadmap*** Introduction to reinforcement learning

    * ***Roadmap*** Deep contextual bandits

    * [Deep Q-networks (DQN)](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter17_deep-reinforcement-learning/DQN.ipynb)

    * [Double-DQN](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter17_deep-reinforcement-learning/DDQN.ipynb)

    * ***Roadmap*** Policy gradient

    * ***Roadmap*** Actor-critic gradient

* **Chapter 18:** Variational methods and uncertainty

    * ***Roadmap*** Dropout-based uncertainty estimation (BALD)

    * [Weight uncertainty (Bayes by Backprop) from scratch](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter18_variational-methods-and-uncertainty/bayes-by-backprop.ipynb)

    * [Weight uncertainty (Bayes by Backprop) with ``gluon``](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter18_variational-methods-and-uncertainty/bayes-by-backprop-gluon.ipynb)

    * [Weight uncertainty (Bayes by Backprop) for Recurrent Neural Networks](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter18_variational-methods-and-uncertainty/bayes-by-backprop-rnn.ipynb)

    * ***Roadmap*** Variational autoencoders

* **Chapter 19:** Graph Neural Networks

    * [Deep Learning on Graphs with Message Passing Neural Networks](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/chapter19_graph-neural-networks/Graph-Neural-Networks.ipynb)

### Appendices

* Appendix 1: Cheatsheets

    * ***Roadmap*** ``gluon``

    * ***Roadmap*** [PyTorch to MXNet](https://github.com/zackchase/mxnet-the-straight-dope/blob/master/cheatsheets/pytorch_gluon.md) (work in progress)

    * ***Roadmap*** Tensorflow to MXNet

    * ***Roadmap*** Keras to MXNet

    * ***Roadmap*** Math to MXNet

## Choose your own adventure

We've designed these tutorials so that you can traverse the curriculum in more than one way.

* Anarchist - Choose whatever you want to read, whenever you want to read it.

* Imperialist - Proceed through all tutorials in order. In this fashion you will be exposed to each model first from scratch, writing all the code ourselves but for the basic linear algebra primitives and automatic differentiation.

* Capitalist - If you don't care how things work (or already know) and just want to see working code in ``gluon``, you can skip (*from scratch!*) tutorials and go straight to the production-like code using the high-level ``gluon`` front end.

## Authors

This evolving creature is a collaborative effort (see contributors tab). The lead writers, assimilators, and coders include:

* Zachary C. Lipton ([@zackchase](https://github.com/zackchase))

* Mu Li ([@mli](https://github.com/mli))

* Alex Smola ([@smolix](https://github.com/smolix))

* Sheng Zha ([@szha](https://github.com/szha))

* Aston Zhang ([@astonzhang](https://github.com/astonzhang))

* Joshua Z. Zhang ([@zhreshold](https://github.com/zhreshold))

* Eric Junyuan Xie ([@piiswrong](https://github.com/piiswrong))

* Kamyar Azizzadenesheli ([@kazizzad](https://github.com/kazizzad))

* Jean Kossaifi ([@JeanKossaifi](https://github.com/JeanKossaifi))

* Stephan Rabanser ([@steverab](https://github.com/steverab))

## Inspiration

In creating these tutorials, we've have drawn inspiration from some the resources that allowed us

to learn deep / machine learning with other libraries in the past. These include:

* [Soumith Chintala's *Deep Learning with PyTorch: A 60 Minute Blitz*](http://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html)

* [Alec Radford's *Bare-bones intro to Theano*](https://github.com/Newmu/Theano-Tutorials)

* [Video of Alec's intro to deep learning with Theano](https://www.youtube.com/watch?v=S75EdAcXHKk)

* [Chris Bishop's *Pattern Recognition and Machine Learning*](https://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738)

## Contribute

* Already, in the short time this project has been off the ground, we've gotten some helpful PRs from the community with pedagogical suggestions, typo corrections, and other useful fixes. If you're inclined, please contribute!