Machine Learning Q and AI. 30 Essential Questions and Answers on Machine Learning and AI
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- Nombre de pages234
- PrésentationBroché
- FormatGrand Format
- 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.
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.
A propos de Sebastian Raschka
Python Machine Learning
2nd edition
Grand Format
50,80 €