Learn the answers to 30 cutting-edge questions in machine learning and AI and level up your expertise in the field. nIf youve 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. n nEach 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 wont need to solve proofs or run code, so this book is a perfect travel companion. Youll 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: n nn n- Reduce overfitting with altered data or model modifications n- Handle common sources of randomness when training deep neural networks n- Speed up model inference through optimization without changing the model architecture or sacrificing accuracy n- Practically apply the lottery ticket hypothesis and the distributional hypothesis n- Use and finetune pretrained large language models n- Set up k-fold cross-validation at the appropriate time nYoull 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. nWhether youre 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.n