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Machine Learning Q and AI. 30 Essential Questions and Answers on Machine Learning and AI
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- Nombre de pages234
- FormatGrand Format
- PrésentationBroché
- Poids0.51 kg
- Dimensions17,5 cm × 23,5 cm × 1,5 cm
- ISBN978-1-7185-0376-2
- EAN9781718503762
- Date de parution16/04/2024
- ÉditeurNo Starch Press
Résumé
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 Al 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 Al 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 Al will make it easy for you to painlessly advance your knowledge beyond the basics.
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 Al 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 Al will make it easy for you to painlessly advance your knowledge beyond the basics.
