pdfcoffee

Preface
Deep Learning with TensorFlow 2 and Keras, Second Edition is a concise yet thorough
introduction to modern neural networks, artificial intelligence, and deep learning
technologies designed especially for software engineers and data scientists. The book
is the natural follow-up of the books Deep Learning with Keras [1] and TensorFlow 1.x
Deep Learning Cookbook [2] previously written by the same authors.
Mission
This book provides a very detailed panorama of the evolution of learning
technologies during the past six years. The book presents dozens of working
deep neural networks coded in Python using TensorFlow 2.0, a modular network
library based on Keras-like [1] APIs.
You are introduced step-by-step to supervised learning algorithms such as simple
linear regression, classical multilayer perceptrons, and more sophisticated deep
convolutional networks and generative adversarial networks. In addition, the book
covers unsupervised learning algorithms such as autoencoders and generative
networks. Recurrent networks and Long Short-Term Memory (LSTM) networks
are also explained in detail. The book also includes a comprehensive introduction
to deep reinforcement learning and it covers deep learning accelerators (GPUs and
TPUs), cloud development, and multi-environment deployment on your desktop,
on the cloud, on mobile/IoT devices, and on your browser.
Practical applications include code for text classification into predefined categories,
syntactic analysis, sentiment analysis, synthetic generation of text, and parts-ofspeech
tagging. Image processing is also explored, with recognition of handwritten
digit images, classification of images into different categories, and advanced object
recognition with related image annotations.
[ xi ]