Publisher's Synopsis
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 modifications; Handle common sources of randomness when training deep neural networks; Speed up model inference through optimization without changing the model architecture or sacrificing accuracy; Practically apply the lottery ticket hypothesis and the distributional hypothesis; Use and finetune pretrained large language models; Set up k-fold cross-validation at the appropriate time. You'll also learn to distinguish between self-attention and regular attention; name the most common data augmentation techniques for text data; use various self-supervised learning techniques, multi-GPU training paradigms, and types of generative AI; and much more. Whether you're a machine learning beginner or an experienced practitioner, add new techniques to your arsenal and keep abreast of exciting developments in a rapidly changing field.
