Deep learning with Python

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Deep learning with Python / François Chollet

Format:: Book
Published:: Shelter Island, N.Y. : Manning, c2018
Description:: xxi, 361 p. ; 24 cm
Authors:: Chollet, François <DA18956221>
ISBN:: 9781617294433 [1617294438] (: pbk)
NCID:: BB25158480

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Bibliography Details

Note:

Later printing has different pagination: xix, 361 p
Includes index

Subject:

Machine learning; Neural networks (Computer science); Python (Computer program language)

Language:

English

Table of Contents:

Preface

Acknowledgments

About this book

About the author

About the cover

Fundamentals of deep learning / Part 1：

What is deep learning? / 1：

Artificial intelligence, machine learning, and deep learning / 1.1：

Artificial intelligence

Machine learning

Learning representations from data

The "deep" in deep learning

Understanding how deep learning works, in three figures

What deep learning has achieved so far

Don't believe the short-term hype

The promise of AI

Before deep learning: a brief history of machine learning / 1.2：

Probabilistic modeling

Early neural networks

Kernel methods

Decision trees, random forests, and gradient boosting machines

Back to neural networks

What makes deep learning different

The modern machine-learning landscape

Why deep learning? Why now? / 1.3：

Hardware

Data

Algorithms

A new wave of investment

The democratization of deep learning

Will it last?

Before we begin: the mathematical building blocks of neural networks / 2：

A first look at a neural network / 2.1：

Data representations for neural networks / 2.2：

Scalars (0D tensors)

Vectors (1D tensors)

Matrices (2D tensors)

3D tensors and higher-dimensional tensors

Key attributes

Manipulating tensors in Numpy

The notion of data batches

Real-world examples of data tensors

Vector data

Timeseries data or sequence data

Image data

Video data

The gears of neural networks: tensor operations / 2.3：

Element-wise operations

Broadcasting

Tensor dot

Tensor reshaping

Geometric interpretation of tensor operations

A geometric interpretation of deep learning

The engine of neural networks: gradient-based optimization / 2.4：

What's a derivative?

Derivative of a tensor operation: the gradient

Stochastic gradient descent

Chaining derivatives: the Backpropagation algorithm

Looking back at our first example / 2.5：

Chapter summary / 2.6：

Getting started with neural networks / 3：

Anatomy of a neural network / 3.1：

Layers: the building blocks of deep learning

Models: networks of layers

Loss functions and optimizers: keys to configuring the learning process

Introduction to Keras / 3.2：

Keras, TensorFlow, Theano, and CNTK

Developing with Keras: a quick overview

Setting up a deep-learning workstation / 3.3：

Jupyter notebooks: the preferred way to run deep-learning experiments

Getting Keras running: two options

Running deep-learning jobs in the cloud: pros and cons

What is the best GPU for deep learning?

Classifying movie reviews: a binary classification example / 3.4：

The IMDB dataset

Preparing the data

Building your network

Validating your approach

Using a trained network to generate predictions on new data

Further experiments

Wrapping up

Classifying newswires: a multiclass classification example / 3.5：

The Reuters dataset

Generating predictions on new data

A different way to handle the labels and the loss

The importance of having sufficiently large intermediate layers

Predicting house prices: a regression example / 3.6：

The Boston Housing Price dataset

Validating your approach using K-fold validation

Fundamentals of machine learning / 3.7：

Four branches of machine learning / 4.1：

Supervised learning

Unsupervised learning

Self-supervised learning

Reinforcement learning

Evaluating machine-learning models / 4.2：

Training validation, and test sets

Things to keep in mind

Data preprocessing, feature engineering, and feature learning / 4.3：

Data preprocessing for neural networks

Feature engineering

Overfitting and underfitting / 4.4：

Reducing the network's size

Adding weight regularization

Adding dropout

The universal workflow of machine learning / 4.5：

Defining the problem and assembling a dataset

Choosing a measure of success

Deciding on an evaluation protocol

Preparing your data

Developing a model that does better than a baseline

Scaling up: developing a model that overfits

Regularizing your model and luning your hyperparameters

Deep Learning in Practice / 4.6：

Deep learning for computer vision / 5：

Introduction to convnets / 5.1：

The convolution operation

The max-pooling operation

Training a convnet from scratch on a small dataset / 5.2：

The relevance of deep learning for small-data problems

Downloading the data

Data preprocessing

Using data augmentation

Using a pretrained convnet / 5.3：

Feature extraction

Fine-luning

Visualizing what convnets learn / 5.4：

Visualizing intermediate activations

Visualizing convnet filters

Visualizing heatmaps of class activation

Deep learning for text and sequences / 5.5：

Working with text data / 6.1：

One-hot encoding of words and characters

Using word embeddings

Putting it all together: from raw text to word embeddings

Understanding recurrent neural networks / 6.2：

A recurrent layer in Keras

Understanding the LSTM and GRU layers

A concrete LSTM example in Keras

Advanced use of recurrent neural networks / 6.3：

A temperature-forecasting problem

A common-sense, non-machine-learning baseline

A basic machine-learning approach

A first recurrent baseline

Using recurrent dropout to fight overfitting

Stacking recurrent layers

Using bidirectional RNNs

Going even further

Sequence processing with convnets / 6.4：

Understanding 1D convolution for sequence data

1D pooling for sequence data

Implementing a 1D convnet

Combining CNNs and RNNs to process long sequences

Advanced deep-learning best practices / 6.5：

Going beyond the Sequential model: the Keras functional API / 7.1：

Introduction to the functional API

Multi-input models

Multi-output models

Directed acyclic graphs of layers

Layer weight sharing

Models as layers

Inspecting and monitoring deep-learning models using Keras callbacks and TensorBoard / 7.2：

Using callbacks to act on a model during training

Introduction to TensorBoard: the TensorFlow visualization framework

Getting the most out of your models / 7.3：

Advanced architecture patterns

Hyperparameter optimization

Model ensembling

Generative deep learning / 7.4：

Text generation with LSTM / 8.1：

A brief history of generative recurrent networks

How do you generate sequence data?

The importance of the sampling strategy

Implementing character-level LSTM text generation

DeepDream / 8.2：

Implementing DeepDream in Keras

Neural style transfer / 8.3：

The content loss

The style loss

Neural style transfer in Keras

Generating images with variational autoencoders / 8.4：

Sampling from latent spaces of images

Concept vectors for image editing

Variational autoencoders

Introduction to generative adversarial networks / 8.5：

A schematic GAN implementation

A bag of tricks

The generator

The discriminator

The adversarial network

How to train your DCGAN

Conclusions / 8.6：

Key concepts in review / 9.1：

Various approaches to AI

What makes deep learning special within the field of machine learning

How to think about deep learning

Key enabling technologies

The universal machine-learning workflow

Key network architectures

The space of possibilities

The limitations of deep learning / 9.2：

The risk of anthropomorphizing machine-learning models

Local generalization vs. extreme generalization

The future of deep learning / 9.3：

Models as programs

Beyond backpropagation and differentiable layers

Automated machine learning

Lifelong learning and modular subroutine reuse

The long-term vision

Staying up to date in a fast-moving field / 9.4：

Practice on real-world problems using Kaggle

Read about the latest developments on arXiv

Explore the Kerns ecosystem

Final words / 9.5：

Installing Keras and its dependencies on Ubuntu / Appendix A：

Running Jupyter notebooks on an EC2 GPU instance / Appendix B：

Index