08-12-2025, 01:09 -
[center]![[Obrazek: 39a21ce0610a18991f98f283cdda90a9.png]](https://i126.fastpic.org/big/2025/1208/a9/39a21ce0610a18991f98f283cdda90a9.png)
English | 2025 | ISBN: 1032944625 | 374 pages | True PDF,EPUB | 64 MB[/center]
This book presents a collection of rigorously revised papers selected from the 22nd IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2023). It focuses on deep learning architectures and their applications in domains such as health care, security and threat detection, education, fault diagnosis, and robotic control in industrial environments. Novel ways of using convolutional neural networks, transformers, autoencoders, graph-based neural networks, and large language models for the above applications are covered in this book. Readers will find insights to help them realize novel ways of using deep learning architectures and models in real-world applications and contexts, making this book an essential reference guide for academic researchers, professionals, software engineers in the industry, and innovative product developers.
Key Features
Presents state-of-the-art research on deep learning
Covers modern real-world applications of deep learning
Provides value to students, academic researchers, professionals, software engineers in the industry, and innovative product developers.
English | 2025 | ISBN: 1032944625 | 374 pages | True PDF,EPUB | 64 MB[/center]
This book presents a collection of rigorously revised papers selected from the 22nd IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2023). It focuses on deep learning architectures and their applications in domains such as health care, security and threat detection, education, fault diagnosis, and robotic control in industrial environments. Novel ways of using convolutional neural networks, transformers, autoencoders, graph-based neural networks, and large language models for the above applications are covered in this book. Readers will find insights to help them realize novel ways of using deep learning architectures and models in real-world applications and contexts, making this book an essential reference guide for academic researchers, professionals, software engineers in the industry, and innovative product developers.
Key Features
Presents state-of-the-art research on deep learning
Covers modern real-world applications of deep learning
Provides value to students, academic researchers, professionals, software engineers in the industry, and innovative product developers.
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