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Dive into Deep Learning
Table Of Contents
Introduction
Preface
Introduction to Deep Learning
Using this Book
A Taste of Deep Learning
Introduction
Getting started with Gluon
Manipulating Data with
ndarray
Linear algebra
Automatic Differentiation
Probability and statistics
Naive Bayes Classification
Sampling
Documentation
Deep Learning Basics
Linear Regression
Linear regression implementation from scratch
Gluon Implementation of Linear Regression
Softmax Regression
Image Classification Data (Fashion-MNIST)
Softmax Regression from Scratch
Softmax Regression in Gluon
Multilayer Perceptron
Implementing a Multilayer Perceptron from Scratch
Multilayer Perceptron in Gluon
Model Selection, Underfitting and Overfitting
Weight Decay
Dropout
Forward Propagation, Back Propagation and Computational Graphs
Numerical Stability and Initialization
Environment
Predicting House Prices on Kaggle
Deep Learning Computation
Layers and Blocks
Parameter Management
Deferred Initialization
Custom Layers
File I/O
GPUs
Convolutional Neural Networks
From Dense Layers to Convolutions
Convolutions for Images
Padding and Stride
Multiple Input and Output Channels
Pooling
Convolutional Neural Networks (LeNet)
Deep Convolutional Neural Networks (AlexNet)
Networks Using Blocks (VGG)
Network in Network (NiN)
Networks with Parallel Concatenations (GoogLeNet)
Batch Normalization
Residual Networks (ResNet)
Densely Connected Networks (DenseNet)
Recurrent Neural Networks
Sequence Models
Language Models
Recurrent Neural Networks
Text Preprocessing
Building a Recurrent Neural Network from Scratch
Gluon Implementation in Recurrent Neural Networks
Back-propagation Through Time
Gated Recurrent Unit (GRU)
Long Short-term Memory (LSTM)
Deep Recurrent Neural Networks
Bidirectional Recurrent Neural Networks
Optimization Algorithms
Optimization and Deep Learning
Gradient Descent and Stochastic Gradient Descent
Mini-Batch Stochastic Gradient Descent
Momentum
Adagrad
RMSProp
Adadelta
Adam
Computational Performance
A Hybrid of Imperative and Symbolic Programming
Asynchronous Computing
Automatic Parallelism
Multi-GPU Computation
Gluon Implementation for Multi-GPU Computation
Computer Vision
Image Augmentation
Fine Tuning
Object Detection and Bounding Box
Anchor Boxes
Multiscale Object Detection
Object Detection Data Set (Pikachu)
Single Shot Multibox Detection (SSD)
Region-based CNNs (R-CNNs)
Semantic Segmentation and Data Sets
Fully Convolutional Networks (FCN)
Neural Style Transfer
Image Classification (CIFAR-10) on Kaggle
Dog Breed Identification (ImageNet Dogs) on Kaggle
Natural Language Processing
Word Embedding (word2vec)
Approximate Training
Implementation of Word2vec
Subword embedding (fastText)
Word Embedding with Global Vectors (GloVe)
Finding Synonyms and Analogies
Text Sentiment Classification: Using Recurrent Neural Networks
Text Sentiment Classification: Using Convolutional Neural Networks (textCNN)
Encoder-Decoder (seq2seq)
Beam Search
Attention Mechanism
Machine Translation
Appendix
List of Main Symbols
Mathematical Basis
Using Jupyter Notebook
Using AWS to Run Code
GPU Purchase Guide
How to Contribute to This Book
Gluonbook
Package Index
Dive into Deep Learning
Table Of Contents
Introduction
Preface
Introduction to Deep Learning
Using this Book
A Taste of Deep Learning
Introduction
Getting started with Gluon
Manipulating Data with
ndarray
Linear algebra
Automatic Differentiation
Probability and statistics
Naive Bayes Classification
Sampling
Documentation
Deep Learning Basics
Linear Regression
Linear regression implementation from scratch
Gluon Implementation of Linear Regression
Softmax Regression
Image Classification Data (Fashion-MNIST)
Softmax Regression from Scratch
Softmax Regression in Gluon
Multilayer Perceptron
Implementing a Multilayer Perceptron from Scratch
Multilayer Perceptron in Gluon
Model Selection, Underfitting and Overfitting
Weight Decay
Dropout
Forward Propagation, Back Propagation and Computational Graphs
Numerical Stability and Initialization
Environment
Predicting House Prices on Kaggle
Deep Learning Computation
Layers and Blocks
Parameter Management
Deferred Initialization
Custom Layers
File I/O
GPUs
Convolutional Neural Networks
From Dense Layers to Convolutions
Convolutions for Images
Padding and Stride
Multiple Input and Output Channels
Pooling
Convolutional Neural Networks (LeNet)
Deep Convolutional Neural Networks (AlexNet)
Networks Using Blocks (VGG)
Network in Network (NiN)
Networks with Parallel Concatenations (GoogLeNet)
Batch Normalization
Residual Networks (ResNet)
Densely Connected Networks (DenseNet)
Recurrent Neural Networks
Sequence Models
Language Models
Recurrent Neural Networks
Text Preprocessing
Building a Recurrent Neural Network from Scratch
Gluon Implementation in Recurrent Neural Networks
Back-propagation Through Time
Gated Recurrent Unit (GRU)
Long Short-term Memory (LSTM)
Deep Recurrent Neural Networks
Bidirectional Recurrent Neural Networks
Optimization Algorithms
Optimization and Deep Learning
Gradient Descent and Stochastic Gradient Descent
Mini-Batch Stochastic Gradient Descent
Momentum
Adagrad
RMSProp
Adadelta
Adam
Computational Performance
A Hybrid of Imperative and Symbolic Programming
Asynchronous Computing
Automatic Parallelism
Multi-GPU Computation
Gluon Implementation for Multi-GPU Computation
Computer Vision
Image Augmentation
Fine Tuning
Object Detection and Bounding Box
Anchor Boxes
Multiscale Object Detection
Object Detection Data Set (Pikachu)
Single Shot Multibox Detection (SSD)
Region-based CNNs (R-CNNs)
Semantic Segmentation and Data Sets
Fully Convolutional Networks (FCN)
Neural Style Transfer
Image Classification (CIFAR-10) on Kaggle
Dog Breed Identification (ImageNet Dogs) on Kaggle
Natural Language Processing
Word Embedding (word2vec)
Approximate Training
Implementation of Word2vec
Subword embedding (fastText)
Word Embedding with Global Vectors (GloVe)
Finding Synonyms and Analogies
Text Sentiment Classification: Using Recurrent Neural Networks
Text Sentiment Classification: Using Convolutional Neural Networks (textCNN)
Encoder-Decoder (seq2seq)
Beam Search
Attention Mechanism
Machine Translation
Appendix
List of Main Symbols
Mathematical Basis
Using Jupyter Notebook
Using AWS to Run Code
GPU Purchase Guide
How to Contribute to This Book
Gluonbook
Package Index
Appendix
¶
List of Main Symbols
Numbers
Sets
Operators
Functions
Derivatives and Gradients
Probability and Statistics
Complexity
Mathematical Basis
Linear Algebra
Differentials
Probability
Summary
Exercises
Discuss on our Forum
Using Jupyter Notebook
Edit and Run the Code in This Book Locally
Advanced Options
Summary
Problem
Discuss on our Forum
Using AWS to Run Code
Apply for an Account and Log In
Create and Run an EC2 Instance
Install CUDA
Acquire the Code for this Book and Install MXNet GPU Version
Run Jupyter Notebook
Close Unused Instances
Summary
Problem
Discuss on our Forum
GPU Purchase Guide
Selecting a GPU
Machine Configuration
Summary
exercise
Discuss on our Forum
How to Contribute to This Book
Summary
Problem
References
Discuss on our Forum
Gluonbook
Package Index
Previous
Machine Translation
Next
List of Main Symbols