Overview

This book provides a conceptual understanding of deep learning algorithms. The book consists of the four parts: foundations, deep machine learning, deep neural networks, and textual deep learning.

Part 1: Foundations

The first part provides traditional supervised learning, traditional unsupervised learning, and ensemble learning, as the preparation for studying deep learning algorithms.

Part 2: Deep Machine Learning

The second part deals with modification of existing machine learning algorithms into deep learning algorithms.

Part 3: Deep Neural Networks

The book’s third part deals with deep neural networks, such as Multiple Perceptron, Recurrent Networks, Restricted Boltzmann Machine, and Convolutionary Neural Networks.

Part 4: Textual Deep Learning

The last part provides deep learning techniques that are specialized for text mining tasks.

Intended Audience

The book is relevant for researchers, academics, students, and professionals in machine learning.