Learn the answers to 30 cutting-edge questions in machine learning and AI and level up your
expertise in the field. If you’re ready to venture beyond introductory concepts and dig
deeper into machine learning deep learning and AI the question-and-answer format of Machine
Learning Q and AI will make things fast and easy for you without a lot of mucking about. Born
out of questions often fielded by author Sebastian Raschka the direct no-nonsense approach of
this book makes advanced topics more accessible and genuinely engaging. Each brief
self-contained chapter journeys through a fundamental question in AI unraveling it with clear
explanations diagrams and hands-on exercises. WHAT'S INSIDE: FOCUSED CHAPTERS: Key
questions in AI are answered concisely and complex ideas are broken down into easily
digestible parts. WIDE RANGE OF TOPICS: Raschka covers topics ranging from neural network
architectures and model evaluation to computer vision and natural language processing.
PRACTICAL APPLICATIONS: Learn techniques for enhancing model performance fine-tuning large
models and more. You’ll also explore how to: • Manage the various sources of randomness in
neural network training • Differentiate between encoder and decoder architectures in large
language models • Reduce overfitting through data and model modifications • Construct
confidence intervals for classifiers and optimize models with limited labeled data • Choose
between different multi-GPU training paradigms and different types of generative AI models •
Understand performance metrics for natural language processing • Make sense of the inductive
biases in vision transformers If you’ve been on the hunt for the perfect resource to elevate
your understanding of machine learning Machine Learning Q and AI will make it easy for you to
painlessly advance your knowledge beyond the basics.
