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