Free E-book read Online : Deep Learning - The Straight Dope

Deep Learning - The Straight Dope

This repo contains an incremental sequence of notebooks designed to teach deep learning, Apache MXNet (incubating), 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.

Part 1: Deep Learning Fundamentals

Crash course

Preface
Introduction
Manipulate data the MXNet way with ndarray
Linear algebra
Intermediate linear algebra
Probability and statistics
Automatic differentiation with autograd

Introduction to supervised learning

Linear regression from scratch
Linear regression with gluon
Binary classification with logistic regression
Multiclass logistic regression from scratch
Multiclass logistic regression with gluon
Overfitting and regularization
Overfitting and regularization (with gluon)
The Perceptron
Environment

Deep neural networks

Multilayer perceptrons from scratch
Multilayer perceptrons in gluon
Faster modeling with gluon.nn.Sequential
Dropout regularization from scratch
Dropout regularization with gluon
Plumbing: A look under the hood of gluon
Designing a custom layer with gluon
Serialization - saving, loading and checkpointing

Convolutional neural networks

Convolutional neural networks from scratch
Convolutional Neural Networks in gluon
Deep convolutional neural networks
Very deep networks with repeating elements
Batch Normalization from scratch
Batch Normalization in gluon

Recurrent neural networks

Recurrent Neural Networks (RNNs) for Language Modeling
Long short-term memory (LSTM) RNNs
Gated recurrent unit (GRU) RNNs
Recurrent Neural Networks with gluon

Optimization

Introduction
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

High-performance and distributed training

Fast, portable neural networks with Gluon HybridBlocks
Training with multiple GPUs from scratch
Training on multiple GPUs with gluon
Distributed training with multiple machines

Part 2: Applications

Computer vision

Object Detection Using Convolutional Neural Networks
Transfering knowledge through finetuning
Visual Question Answering in gluon

Natural language processing

Tree LSTM modeling for semantic relatedness
Recommender systems
Introduction to recommender systems

Time series

Linear Dynamical Systems with MXNet
Filtering
Generating Synthetic Dataset
Exponential Smoothing and Innovation State Space Model (ISSM)
Filtering

Part 3: Advanced Topics

Generative adversarial networks

Generative Adversarial Networks
Deep Convolutional Generative Adversarial Networks
Pixel to Pixel Generative Adversarial Networks

Variational methods

Bayes by Backprop from scratch (NN, classification)
Bayes by Backprop with gluon (NN, classification)

Header Ads

Free E-book read Online : Deep Learning - The Straight Dope