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eBay-Artikelnr.:355516309610
Artikelmerkmale
- Artikelzustand
- Book Title
- Demystifying Deep Learning : An Introduction to the Mathematics o
- ISBN
- 9781394205608
- Subject Area
- Computers, Science
- Publication Name
- Demystifying Deep Learning : an Introduction to the Mathematics of Neural Networks
- Publisher
- Wiley & Sons, Incorporated, John
- Item Length
- 9 in
- Subject
- Neural Networks, General
- Publication Year
- 2023
- Type
- Textbook
- Format
- Hardcover
- Language
- English
- Item Height
- 0.7 in
- Item Weight
- 22 Oz
- Item Width
- 6 in
- Number of Pages
- 256 Pages
Über dieses Produkt
Product Identifiers
Publisher
Wiley & Sons, Incorporated, John
ISBN-10
1394205600
ISBN-13
9781394205608
eBay Product ID (ePID)
25060615269
Product Key Features
Number of Pages
256 Pages
Publication Name
Demystifying Deep Learning : an Introduction to the Mathematics of Neural Networks
Language
English
Publication Year
2023
Subject
Neural Networks, General
Type
Textbook
Subject Area
Computers, Science
Format
Hardcover
Dimensions
Item Height
0.7 in
Item Weight
22 Oz
Item Length
9 in
Item Width
6 in
Additional Product Features
Intended Audience
College Audience
LCCN
2023-047936
Dewey Edition
23
Reviews
I recently read DEMYSTIFYING DEEP LEARNING and it really exceeded my expectations! It is incredibly comprehensive and well organized with plenty of references to examples available on the books website. The amount of knowledge in the book makes it a must-have for anyone interested in deep learning. As someone who had only basic knowledge of the subject, the numerous examples available were invaluable in understanding complex concepts.Alessandro Migliaccio, President, AiShed|9781394205608|
Dewey Decimal
006.310151
Synopsis
DEMYSTIFYING DEEP LEARNING Discover how to train Deep Learning models by learning how to build real Deep Learning software libraries and verification software! The study of Deep Learning and Artificial Neural Networks (ANN) is a significant subfield of artificial intelligence (AI) that can be found within numerous fields: medicine, law, financial services, and science, for example. Just as the robot revolution threatened blue-collar jobs in the 1970s, so now the AI revolution promises a new era of productivity for white collar jobs. Important tasks have begun being taken over by ANNs, from disease detection and prevention, to reading and supporting legal contracts, to understanding experimental data, model protein folding, and hurricane modeling. AI is everywhere--on the news, in think tanks, and occupies government policy makers all over the world --and ANNs often provide the backbone for AI. Relying on an informal and succinct approach, Demystifying Deep Learning is a useful tool to learn the necessary steps to implement ANN algorithms by using both a software library applying neural network training and verification software. The volume offers explanations of how real ANNs work, and includes 6 practical examples that demonstrate in real code how to build ANNs and the datasets they need in their implementation, available in open-source to ensure practical usage. This approachable book follows ANN techniques that are used every day as they adapt to natural language processing, image recognition, problem solving, and generative applications. This volume is an important introduction to the field, equipping the reader for more advanced study. Demystifying Deep Learning readers will also find: A volume that emphasizes the importance of classification Discussion of why ANN libraries, such as Tensor Flow and Pytorch, are written in C++ rather than Python Each chapter concludes with a "Projects" page to promote students experimenting with real code A supporting library of software to accompany the book at https://github.com/nom-de-guerre/RANT An approachable explanation of how generative AI, such as generative adversarial networks (GAN), really work. An accessible motivation and elucidation of how transformers, the basis of large language models (LLM) such as ChatGPT, work. Demystifying Deep Learning is ideal for engineers and professionals that need to learn and understand ANNs in their work. It is also a helpful text for advanced undergraduates to get a solid grounding on the topic., DEMYSTIFYING DEEP LEARNING Discover how to train Deep Learning models by learning how to build real Deep Learning software libraries and verification software! The study of Deep Learning and Artificial Neural Networks (ANN) is a significant subfield of artificial intelligence (AI) that can be found within numerous fields: medicine, law, financial services, and science, for example. Just as the robot revolution threatened blue-collar jobs in the 1970s, so now the AI revolution promises a new era of productivity for white collar jobs. Important tasks have begun being taken over by ANNs, from disease detection and prevention, to reading and supporting legal contracts, to understanding experimental data, model protein folding, and hurricane modeling. AI is everywhere--on the news, in think tanks, and occupies government policy makers all over the world --and ANNs often provide the backbone for AI. Relying on an informal and succinct approach, Demystifying Deep Learning is a useful tool to learn the necessary steps to implement ANN algorithms by using both a software library applying neural network training and verification software. The volume offers explanations of how real ANNs work, and includes 6 practical examples that demonstrate in real code how to build ANNs and the datasets they need in their implementation, available in open-source to ensure practical usage. This approachable book follows ANN techniques that are used every day as they adapt to natural language processing, image recognition, problem solving, and generative applications. This volume is an important introduction to the field, equipping the reader for more advanced study. Demystifying Deep Learning readers will also find: A volume that emphasizes the importance of classification Discussion of why ANN libraries, such as Tensor Flow and Pytorch, are written in C++ rather than Python Each chapter concludes with a "Projects" page to promote students experimenting with real code A supporting library of software to accompany the book at https: //github.com/nom-de-guerre/RANT An approachable explanation of how generative AI, such as generative adversarial networks (GAN), really work. An accessible motivation and elucidation of how transformers, the basis of large language models (LLM) such as ChatGPT, work. Demystifying Deep Learning is ideal for engineers and professionals that need to learn and understand ANNs in their work. It is also a helpful text for advanced undergraduates to get a solid grounding on the topic.
LC Classification Number
Q325.73.S36 2024
ebay_catalog_id
4
Copyright Date
2024
Artikelbeschreibung des Verkäufers
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eBay-Artikelnr.:355516309610
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Artikelstandort:
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