Nouveauté
Fundamentals of Machine Learning: a Simplified Approach
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- FormatePub
- ISBN978-93-6418-344-4
- EAN9789364183444
- Date de parution29/06/2025
- Protection num.pas de protection
- Infos supplémentairesepub
- ÉditeurBLUE DUCK PUBLICATIONS
Résumé
This book aims to serve as a comprehensive introduction to the principles, algorithms, and applications of machine learning, with a particular emphasis on both foundational and cutting-edge techniques. It is designed for students, researchers, and practitioners who wish to deepen their understanding of machine learning concepts, explore their practical applications, and grasp the underlying mathematical frameworks.
The content spans a broad range of topics, beginning with fundamental machine learning concepts, such as regression, decision trees, and artificial neural networks. It then delves into specialized domains like support vector machines, reinforcement learning, and convolutional neural networks. Along the way, readers will encounter detailed explanations of key algorithms, including ID3, Q-learning, and deep Q-learning, as well as practical insights into their implementation.
Real-world case studies-such as diabetic retinopathy diagnosis using convolutional neural networks, building smart speakers, and self-driving cars-illustrate the transformative potential of these techniques.
The content spans a broad range of topics, beginning with fundamental machine learning concepts, such as regression, decision trees, and artificial neural networks. It then delves into specialized domains like support vector machines, reinforcement learning, and convolutional neural networks. Along the way, readers will encounter detailed explanations of key algorithms, including ID3, Q-learning, and deep Q-learning, as well as practical insights into their implementation.
Real-world case studies-such as diabetic retinopathy diagnosis using convolutional neural networks, building smart speakers, and self-driving cars-illustrate the transformative potential of these techniques.
This book aims to serve as a comprehensive introduction to the principles, algorithms, and applications of machine learning, with a particular emphasis on both foundational and cutting-edge techniques. It is designed for students, researchers, and practitioners who wish to deepen their understanding of machine learning concepts, explore their practical applications, and grasp the underlying mathematical frameworks.
The content spans a broad range of topics, beginning with fundamental machine learning concepts, such as regression, decision trees, and artificial neural networks. It then delves into specialized domains like support vector machines, reinforcement learning, and convolutional neural networks. Along the way, readers will encounter detailed explanations of key algorithms, including ID3, Q-learning, and deep Q-learning, as well as practical insights into their implementation.
Real-world case studies-such as diabetic retinopathy diagnosis using convolutional neural networks, building smart speakers, and self-driving cars-illustrate the transformative potential of these techniques.
The content spans a broad range of topics, beginning with fundamental machine learning concepts, such as regression, decision trees, and artificial neural networks. It then delves into specialized domains like support vector machines, reinforcement learning, and convolutional neural networks. Along the way, readers will encounter detailed explanations of key algorithms, including ID3, Q-learning, and deep Q-learning, as well as practical insights into their implementation.
Real-world case studies-such as diabetic retinopathy diagnosis using convolutional neural networks, building smart speakers, and self-driving cars-illustrate the transformative potential of these techniques.