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-of-speech 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.

Sound analysis comprises the recognition of discrete speech from multiple speakers. Generation of images using Autoencoders and GANs is also covered. Reinforcement learning is used to build a deep Q-learning network capable of learning autonomously. Experiments are the essence of the book. Each net is augmented by multiple variants that progressively improve the learning performance by changing the input parameters, the shape of the network, loss functions, and algorithms used for optimizations. Several comparisons between training on CPUs, GPUs and TPUs are also provided. The book introduces you to the new field of AutoML where deep learning models are used to learn how to efficiently and automatically learn how to build deep learning models. One advanced chapter is devoted to the mathematical foundation behind machine learning.