| 书目名称 | Pro Deep Learning with TensorFlow 2.0 | | 副标题 | A Mathematical Appro | | 编辑 | Santanu Pattanayak | | 视频video | | | 概述 | Teaches how to deploy deep learning applications using TensorFlow 2.0 in a relatively short period of time.Explains different deep learning methods for supervised and unsupervised machine learning.Cov | | 图书封面 |  | | 描述 | .This book builds upon the foundations established in its first edition, with updated chapters and the latest code implementations to bring it up to date with Tensorflow 2.0...Pro Deep Learning with TensorFlow 2.0. begins with the mathematical and core technical foundations of deep learning. Next, you will learn about convolutional neural networks, including new convolutional methods such as dilated convolution, depth-wise separable convolution, and their implementation. You’ll then gain an understanding of natural language processing in advanced network architectures such as transformers and various attention mechanisms relevant to natural language processing and neural networks in general. As you progress through the book, you’ll explore unsupervised learning frameworks that reflect the current state of deep learning methods, such as autoencoders and variational autoencoders. The final chapter covers the advanced topic of generative adversarial networks and their variants, such as cycle consistency GANs and graph neural network techniques such as graph attention networks and GraphSAGE...Upon completing this book, you will understand the mathematical foundations and concepts of de | | 出版日期 | Book 2023Latest edition | | 关键词 | Machine Learning; Deep Learning; Python; TensorFlow; Convolutional Neural networks; Recurrent Neural Netw | | 版次 | 2 | | doi | https://doi.org/10.1007/978-1-4842-8931-0 | | isbn_softcover | 978-1-4842-8930-3 | | isbn_ebook | 978-1-4842-8931-0 | | copyright | Santanu Pattanayak 2023 |
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发表于 2025-3-21 17:35:20
