Deep Learning - The Straight Dope

Note: [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/

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. You'll also want to install Jupyter 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. 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 and via GitHub source.

Table of contents

Part 1: Deep Learning Fundamentals

• Chapter 1: Crash course

  • Preface
  • Introduction
  • Manipulating data with NDArray
  • Linear algebra
  • Probability and statistics
  • Automatic differentiation via autograd

• Chapter 2: Introduction to supervised learning

  • Linear regression (from scratch)
  • Linear regression (with gluon)
  • Binary classification with logistic regression (gluon w bespoke loss function)
  • Multiclass logistic regression (from scratch)
  • Multiclass logistic regression (with gluon)
  • Overfitting and regularization (from scratch)
  • Overfitting and regularization (with gluon)
  • Perceptron and SGD primer
  • Learning environments

• Chapter 3: Deep neural networks (DNNs)

  • Multilayer perceptrons (from scratch)
  • Multilayer perceptrons (with gluon)
  • Dropout regularization (from scratch)
  • Dropout regularization (with gluon)
  • Introduction to gluon.Block and gluon.nn.Sequential()
  • Writing custom layers with gluon.Block
  • Serialization: saving and loading models
  • Advanced Data IO
  • Debugging your neural networks

• Chapter 4: Convolutional neural networks (CNNs)

  • Convolutional neural networks (from scratch)
  • Convolutional neural networks (with gluon)
  • Introduction to deep CNNs (AlexNet)
  • Very deep networks and repeating blocks (VGG network)
  • Batch normalization (from scratch)
  • Batch normalization (with gluon)

• Chapter 5: Recurrent neural networks (RNNs)

  • Simple RNNs (from scratch)
  • LSTMS RNNs (from scratch)
  • GRUs (from scratch)
  • RNNs (with gluon)
  • Roadmap Dropout for recurrent nets
  • Roadmap Zoneout regularization

• Chapter 6: Optimization

  • Introduction to optimization
  • Gradient descent and stochastic gradient descent from scratch
  • Gradient descent and stochastic gradient descent with gluon
  • Momentum from scratch
  • Momentum with gluon
  • Adagrad from scratch
  • Adagrad with gluon
  • RMSprop from scratch
  • RMSprop with gluon
  • Adadelta from scratch
  • Adadelta with gluon
  • Adam from scratch
  • Adam with gluon

• Chapter 7: Distributed & high-performance learning

  • Fast & flexible: combining imperative & symbolic nets with HybridBlocks
  • Training with multiple GPUs (from scratch)
  • Training with multiple GPUs (with gluon)
  • Training with multiple machines
  • 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
  • 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
  • Fine-tuning

• 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

• 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
  • 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)
  • Generalized Linear Models/MLP for Forecasting (with gluon)
  • Roadmap Factor Models for Forecasting
  • Roadmap Recurrent Neural Network for Forecasting
  • Linear Dynamical System (from scratch)
  • Exponential Smoothing and Innovative State-space modeling (from scratch)
  • 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
  • Roadmap Clustering

• Chapter 14: Generative adversarial networks (GANs)

  • Introduction to GANs
  • Deep convolutional GANs (DCGANs)
  • Roadmap Wasserstein-GANs
  • Roadmap Energy-based GANS
  • Roadmap Conditional GANs
  • Image transduction GANs (Pix2Pix)
  • 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
  • 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)
  • Double-DQN
  • 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
  • Weight uncertainty (Bayes by Backprop) with gluon
  • Weight uncertainty (Bayes by Backprop) for Recurrent Neural Networks
  • Roadmap Variational autoencoders

• Chapter 19: Graph Neural Networks

  • Deep Learning on Graphs with Message Passing Neural Networks

Appendices

• Appendix 1: Cheatsheets
  • Roadmap gluon
  • Roadmap PyTorch to MXNet (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)
• Mu Li (@mli)
• Alex Smola (@smolix)
• Sheng Zha (@szha)
• Aston Zhang (@astonzhang)
• Joshua Z. Zhang (@zhreshold)
• Eric Junyuan Xie (@piiswrong)
• Kamyar Azizzadenesheli (@kazizzad)
• Jean Kossaifi (@JeanKossaifi)
• Stephan Rabanser (@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
• Alec Radford's Bare-bones intro to Theano
• Video of Alec's intro to deep learning with Theano
• Chris Bishop's Pattern Recognition and Machine Learning

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!