Description
Book SynopsisThe book reviews core concepts of machine learning (ML) while focusing on modern applications. It is aimed at those who want to advance their understanding of ML by providing technical and practical insights. It does not use complicated mathematics to explain how to benefit from ML algorithms. Unlike the existing literature, this work provides the core concepts with emphasis on fresh ideas and real application scenarios. It starts with the basic concepts of ML and extends the concepts to the different deep learning algorithms. The book provides an introduction and main elements of evaluation tools with Python and walks you through the recent applications of ML in self-driving cars, cognitive decision making, communication networks, security, and signal processing. The concept of generative networks is also presented and focuses on GANs as a tool to improve the performance of existing algorithms.
In summary, this book provides a comprehensive technological path from fundamental
Table of Contents
1. Introduction 2. Linear Algebra 3. Machine Learning 4. Some Practical Notes 5. Deep Learning 6. Generative Adversarial Networks 7. Implementation
