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Deep Learning - Shriram K Vasudevan, Sini Raj Pulari, Subashri Vasudevan

Deep Learning

A Comprehensive Guide
Buch | Softcover
290 Seiten
2024
Chapman & Hall/CRC (Verlag)
978-1-032-02885-9 (ISBN)
CHF 95,95 inkl. MwSt
This book focuses on all the relevant topics of Deep Learning. It covers the conceptual, mathematical and practical aspects of deep learning & offers real time practical examples & case studies. It is aimed primarily at graduates, researchers and professionals working in Deep Learning.
Deep Learning: A Comprehensive Guide provides comprehensive coverage of Deep Learning (DL) and Machine Learning (ML) concepts. DL and ML are the most sought-after domains, requiring a deep understanding – and this book gives no less than that. This book enables the reader to build innovative and useful applications based on ML and DL. Starting with the basics of neural networks, and continuing through the architecture of various types of CNNs, RNNs, LSTM, and more till the end of the book, each and every topic is given the utmost care and shaped professionally and comprehensively.

Key Features






Includes the smooth transition from ML concepts to DL concepts



Line-by-line explanations have been provided for all the coding-based examples



Includes a lot of real-time examples and interview questions that will prepare the reader to take up a job in ML/DL right away



Even a person with a non-computer-science background can benefit from this book by following the theory, examples, case studies, and code snippets



Every chapter starts with the objective and ends with a set of quiz questions to test the reader’s understanding



Includes references to the related YouTube videos that provide additional guidance

AI is a domain for everyone. This book is targeted toward everyone irrespective of their field of specialization. Graduates and researchers in deep learning will find this book useful.

1. Introduction to Deep Learning. 2. The Tools and Prerequisites. 3. Machine Learning: The Fundamentals 4. The Deep Learning Framework. 5. CNN– Convolutional Neural Networks – A Complete Understanding. 6. CNN Architectures – An Evolution 7. Recurrent Neural Networks. 8. Autoencoders. 9. Generative Models. 10. Transfer Learning. 11. Intel OpenVino – A Must Know Deep Learning Toolkit. 12. Interview Questions and Answers.

Erscheinungsdatum
Zusatzinfo 19 Tables, black and white; 178 Line drawings, black and white; 83 Halftones, black and white; 261 Illustrations, black and white
Sprache englisch
Maße 156 x 234 mm
Gewicht 566 g
Themenwelt Informatik Datenbanken Data Warehouse / Data Mining
Informatik Theorie / Studium Künstliche Intelligenz / Robotik
ISBN-10 1-032-02885-8 / 1032028858
ISBN-13 978-1-032-02885-9 / 9781032028859
Zustand Neuware
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