Machine Learning Q and AI : 30 questions et réponses essentielles sur l'IA - Sebastian Raschka - Marjane Mall - Image 1
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Machine Learning Q and AI : 30 questions et réponses essentielles sur l'IA - Sebastian Raschka

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Plongez au cœur de l'intelligence artificielle avec cet ouvrage de référence signé Sebastian Raschka. Conçu pour ceux qui souhaitent dépasser les concepts fondamentaux, ce livre propose 30 questions-réponses ciblées sur le machine learning, le deep learning et l'IA. À travers 264 pages, l'auteur décortique des sujets complexes comme les architectur...

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Description produit

Marque
GENERIC
Titre
Machine Learning Q and AI: 30 Essential Questions and Answers on Machine Learning and AI
Éditeur
No Starch Press
Type de produit
Paperback
Présentation du livre
Paperback
Date de sortie
4/16/2024 12:00:00 AM
Langue d'origine
English
ISBN
1718503768
Nombre de pages
264 pages
Langue
English
Résumé
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. Read more
Auteur
Sebastian Raschka
Date de parution
4/16/2024 12:00:00 AM