Book Image

Deep Learning with TensorFlow and Keras – 3rd edition - Third Edition

By : Amita Kapoor, Antonio Gulli, Sujit Pal
5 (2)
Book Image

Deep Learning with TensorFlow and Keras – 3rd edition - Third Edition

5 (2)
By: Amita Kapoor, Antonio Gulli, Sujit Pal

Overview of this book

Deep Learning with TensorFlow and Keras teaches you neural networks and deep learning techniques using TensorFlow (TF) and Keras. You'll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available. TensorFlow 2.x focuses on simplicity and ease of use, with updates like eager execution, intuitive higher-level APIs based on Keras, and flexible model building on any platform. This book uses the latest TF 2.0 features and libraries to present an overview of supervised and unsupervised machine learning models and provides a comprehensive analysis of deep learning and reinforcement learning models using practical examples for the cloud, mobile, and large production environments. This book also shows you how to create neural networks with TensorFlow, runs through popular algorithms (regression, convolutional neural networks (CNNs), transformers, generative adversarial networks (GANs), recurrent neural networks (RNNs), natural language processing (NLP), and graph neural networks (GNNs)), covers working example apps, and then dives into TF in production, TF mobile, and TensorFlow with AutoML.

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Table of Contents (23 chapters)
Preface
Preface
Who this book is for
What this book covers
Get in touch
References
1
Neural Network Foundations with TF
Neural Network Foundations with TF
What is TensorFlow (TF)?
What is Keras?
Introduction to neural networks
Perceptron
Multi-layer perceptron: our first example of a network
A real example: recognizing handwritten digits
Regularization
Playing with Google Colab: CPUs, GPUs, and TPUs
Sentiment analysis
Predicting output
A practical overview of backpropagation
What have we learned so far?
Toward a deep learning approach
Summary
References
Free Chapter
2
Regression and Classification
Regression and Classification
What is regression?
Prediction using linear regression
Neural networks for linear regression
Classification tasks and decision boundaries
Summary
References
3
Convolutional Neural Networks
Convolutional Neural Networks
Deep convolutional neural networks
An example of DCNN: LeNet
Recognizing CIFAR-10 images with deep learning
Very deep convolutional networks for large-scale image recognition
Deep Inception V3 for transfer learning
Other CNN architectures
Style transfer
Summary
References
4
Word Embeddings
Word Embeddings
Word embedding ‒ origins and fundamentals
Distributed representations
Static embeddings
Creating your own embeddings using Gensim
Exploring the embedding space with Gensim
Using word embeddings for spam detection
Neural embeddings – not just for words
Character and subword embeddings
Dynamic embeddings
Sentence and paragraph embeddings
Language model-based embeddings
Summary
References
5
Recurrent Neural Networks
Recurrent Neural Networks
The basic RNN cell
RNN cell variants
RNN variants
RNN topologies
Encoder-decoder architecture – seq2seq
Attention mechanism
Summary
References
6
Transformers
Transformers
Architecture
Transformers’ architectures
Pretraining
An overview of popular and well-known models
Implementation
Evaluation
Optimization
Common pitfalls: dos and don’ts
The future of transformers
Summary
7
Unsupervised Learning
Unsupervised Learning
Principal component analysis
K-means clustering
Self-organizing maps
Restricted Boltzmann machines
Summary
References
8
Autoencoders
Autoencoders
Introduction to autoencoders
Vanilla autoencoders
Sparse autoencoder
Denoising autoencoders
Stacked autoencoder
Variational autoencoders
Summary
References
9
Generative Models
Generative Models
What is a GAN?
Deep convolutional GAN (DCGAN)
Some interesting GAN architectures
Cool applications of GANs
CycleGAN in TensorFlow
Flow-based models for data generation
Diffusion models for data generation
Summary
References
10
Self-Supervised Learning
Self-Supervised Learning
Previous work
Self-supervised learning
Self-prediction
Contrastive learning
Pretext tasks
Summary
References
11
Reinforcement Learning
Reinforcement Learning
An introduction to RL
Simulation environments for RL
An introduction to OpenAI Gym
Deep Q-networks
Deep deterministic policy gradient
Summary
References
12
Probabilistic TensorFlow
Probabilistic TensorFlow
TensorFlow Probability
TensorFlow Probability distributions
Summary
References
13
An Introduction to AutoML
An Introduction to AutoML
What is AutoML?
Achieving AutoML
Automatic data preparation
Automatic feature engineering
Automatic model generation
AutoKeras
Google Cloud AutoML and Vertex AI
Summary
References
14
The Math Behind Deep Learning
The Math Behind Deep Learning
History
Some mathematical tools
Activation functions
Backpropagation
A note on TensorFlow and automatic differentiation
Summary
References
15
Tensor Processing Unit
Tensor Processing Unit
C/G/T processing units
Four generations of TPUs, plus Edge TPU
TPU performance
How to use TPUs with Colab
Using pretrained TPU models
Summary
References
16
Other Useful Deep Learning Libraries
Other Useful Deep Learning Libraries
Hugging Face
OpenAI
PyTorch
ONNX
H2O.ai
Summary
17
Graph Neural Networks
Graph Neural Networks
Graph basics
Graph machine learning
Graph convolutions – the intuition behind GNNs
Common graph layers
Common graph applications
Graph customizations
Future directions
Summary
References
18
Machine Learning Best Practices
Machine Learning Best Practices
The need for best practices
Data best practices
Model best practices
Summary
References
19
TensorFlow 2 Ecosystem
TensorFlow 2 Ecosystem
TensorFlow Hub
TensorFlow Datasets
TensorFlow Lite
Pretrained models in TensorFlow Lite
An overview of federated learning at the edge
TensorFlow.js
Summary
References
20
Advanced Convolutional Neural Networks
Advanced Convolutional Neural Networks
Composing CNNs for complex tasks
Application zoos with tf.Keras and TensorFlow Hub
Answering questions about images (visual Q&A)
Creating a DeepDream network
Inspecting what a network has learned
Video
Text documents
Audio and music
A summary of convolution operations
Capsule networks
Summary
References
21
Other Books You May Enjoy
Other Books You May Enjoy
22
Index
Index

Perceptron

The “perceptron” is a simple algorithm that, given an input vector x of m values (x1, x2,..., and xm), often called input features or simply features, outputs either a 1 (“yes”) or a 0 (“no”). Mathematically, we define a function:

Where w is a vector of weights, is the dot product , and b is the bias. If you remember elementary geometry, wx + b defines a boundary hyperplane that changes position according to the values assigned to w and b. Note that a hyperplane is a subspace whose dimension is one fewer than that of its ambient space. See (Figure 1.2) for an example:

Figure 1.2: An example of a hyperplane

In other words, this is a very simple but effective algorithm! For example, given three input features, the amounts of red, green, and blue in a color, the perceptron could try to decide whether the color is “white” or not.

Note that the perceptron cannot express a “maybe”...

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