The book assumes some basic knowledge of Python, machine learning, and neural networks, and teaches readers how to use DRL to solve complex and realistic problems. The book follows a learn-by ...

Though this book is new to the shelves, the concepts it presents are classic. For thousands of years, philosophers have written about habits that promote excellence. Abela distills centuries of wisdom ...

In this book you will learn how to align on ML strategies in a team setting, as well as how to set up development (dev) sets and test sets. Recommendations for how to set up dev/test sets have been ...

What is this book about? Implementing Machine learning (ML) and Artificial Intelligence (AI) in the cloud had not been possible earlier due to the lack of processing power and storage. However, Azure ...

Dealing with non-stationarity is one of modern machine learning's greatest challenges. This book focuses on a specific non-stationary environment known as covariate shift, in which the distributions ...

In later chapters, you will prepare data, build and deploy machine learning models, and automate workflow tasks using the same platform. Finally, the exercises in this book will help you get hands-on ...

Additionally, there are books on machine learning theory, practice, and examples (e.g. Introduction to Machine Learning with Python, Hands-on Machine Learning with Scikit-Learn and TensorFlow ...

Chris is the author of two highly cited and widely adopted machine learning text books: Neural Networks for Pattern Recognition (1995) and Pattern Recognition and Machine Learning (2006). He has also ...