With an increasing interest in AI around the world, deep learning has attracted a great deal of public attention. Every day, deep learning algorithms are used across different industries. Deep learning has provided a revolutionary step to actualize AI. While it is a revolutionary technique, deep learning is often thought to be complicated, and so it is often kept from much being known of its contents. Theories and concepts based on deep learning are not complex or difficult. In this book,
we’ll take a step-by-step approach to learn theories and equations for the correct understanding of deep learning. You will find implementations from scratch, with detailed explanations of the cautionary notes for practical use cases.
What this book covers
Chapter 1, Deep Learning Overview, explores how deep learning has evolved.
Chapter 2, Algorithms for Machine Learning – Preparing for Deep Learning, implements machine learning algorithms related to deep learning.
Chapter 3, Deep Belief Nets and Stacked Denoising Autoencoders, dives into Deep Belief Nets and Stacked Denoising Autoencoders algorithms.
Chapter 4, Dropout and Convolutional Neural Networks, discovers more deep learning algorithms with Dropout and Convolutional Neural Networks.
Chapter 5, Exploring Java Deep Learning Libraries – DL4J, ND4J, and More, gains an
insight into the deep learning library, DL4J, and its practical uses.
Chapter 6, Approaches to Practical Applications – Recurrent Neural Networks and More, lets you devise strategies to use deep learning algorithms and libraries in the real world.
