Sinossi
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.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
Informazioni sull?autore
Sebastian Raschka, PhD, is a machine learning and AI researcher with a passion for education. As Lead AI Educator at Lightning AI, he is excited about making AI and deep learning more accessible. Raschka previously was Assistant Professor of Statistics at the University of Wisconsin-Madison, where he specialized in researching deep learning and machine learning, and is the author of the bestselling books Python Machine Learning and Machine Learning with PyTorch and Scikit-Learn. You can find out more about his research on his website at https://sebastianraschka.com.
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
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Machine Learning Q and AI: 30 Essential Questions and Answers on Machine Learning and AI
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Paperback. Condizione: new. Paperback. Learn the answers to 30 cutting-edge questions in machine learning and AI and level up your expertise in the field.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 transformersIf 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. "An advanced exploration of machine learning and AI, with each chapter asking and answering a question from the field. Divided into five sections: deep learning and neural networks; computer vision; natural language processing; production and deployment; and predictive performance and model evaluation"-- Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9781718503762
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Machine Learning and AI Beyond the Basics [Paperback] Raschka, Sebastian
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Paperback. Condizione: New. 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. Codice articolo LU-9781718503762
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