If you've locked down the basics of machine learning and AI and want a fun way to address lingering knowledge gaps, this book is for you. This rapid-fire series of short chapters addresses 30 essential questions in the field, helping you stay current on the latest technologies you can implement in your own work. Each chapter of Machine Learning and AI Beyond the Basics asks and answers a central question, with diagrams to explain new concepts and ample references for further reading. This practical, cutting-edge information is missing from most introductory coursework, but critical for real-world applications, research, and acing technical interviews. You won't need to solve proofs or run code, so this book is a perfect travel companion. You'll learn a wide range of new concepts in deep neural network architectures, computer vision, natural language processing, production and deployment, and model evaluation, including how to: Reduce overfitting with altered data or model modification