Pro Deep Learning with Tensorflow 2.0: A Mathematical Approach to Advanced Artificial Intelligence in Python
暫譯: 深入學習 TensorFlow 2.0:Python 中高級人工智慧的數學方法
Pattanayak, Santanu
- 出版商: Apress
- 出版日期: 2023-01-01
- 售價: $1,990
- 貴賓價: 9.5 折 $1,891
- 語言: 英文
- 頁數: 652
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1484289307
- ISBN-13: 9781484289303
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相關分類:
Python、程式語言、DeepLearning、TensorFlow、人工智慧
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商品描述
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 deep learning, and be able to use the prototypes demonstrated to build new deep learning applications.
What You Will Learn
- Understand full-stack deep learning using TensorFlow 2.0
- Gain an understanding of the mathematical foundations of deep learning
- Deploy complex deep learning solutions in production using TensorFlow 2.0
- Understand generative adversarial networks, graph attention networks, and GraphSAGE
Who This Book Is For:
Data scientists and machine learning professionals, software developers, graduate students, and open source enthusiasts.商品描述(中文翻譯)
這本書建立在第一版所奠定的基礎上,更新了章節並包含最新的程式碼實作,以使其與 TensorFlow 2.0 保持同步。《Pro Deep Learning with TensorFlow 2.0》首先介紹深度學習的數學和核心技術基礎。接下來,您將學習卷積神經網絡,包括新的卷積方法,如擴張卷積(dilated convolution)、深度可分離卷積(depth-wise separable convolution)及其實作。然後,您將了解自然語言處理在先進網絡架構中的應用,例如變壓器(transformers)和與自然語言處理及神經網絡相關的各種注意力機制。隨著您逐步深入本書,您將探索反映當前深度學習方法狀態的無監督學習框架,如自編碼器(autoencoders)和變分自編碼器(variational autoencoders)。最後一章涵蓋生成對抗網絡(generative adversarial networks)及其變體,如循環一致性 GAN(cycle consistency GANs)和圖神經網絡技術,如圖注意力網絡(graph attention networks)和 GraphSAGE。
完成本書後,您將理解深度學習的數學基礎和概念,並能夠使用所示範的原型來構建新的深度學習應用。
您將學到的內容:
- 理解使用 TensorFlow 2.0 的全棧深度學習
- 獲得深度學習的數學基礎知識
- 使用 TensorFlow 2.0 在生產環境中部署複雜的深度學習解決方案
- 理解生成對抗網絡、圖注意力網絡和 GraphSAGE
本書適合對象:
數據科學家和機器學習專業人士、軟體開發人員、研究生以及開源愛好者。
作者簡介
Santanu Pattanayak works as a Senior Staff Machine Learning Specialist at Qualcomm Corp R&D and is the author of Quantum Machine Learning with Python, published by Apress. He has more than 16 years of experience, having worked at GE, Capgemini, and IBM before joining Qualcomm. He graduated with a degree in electrical engineering from Jadavpur University, Kolkata and is an avid math enthusiast. Santanu has a master's degree in data science from the Indian Institute of Technology (IIT), Hyderabad. He also participates in Kaggle competitions in his spare time, where he ranks in the top 500. Currently, he resides in Bangalore with his wife.
作者簡介(中文翻譯)
**Santanu Pattanayak** 目前擔任高通公司(Qualcomm Corp)研發部門的高級員工機器學習專家,並且是由 Apress 出版的《Quantum Machine Learning with Python》的作者。他擁有超過 16 年的經驗,曾在 GE、Capgemini 和 IBM 工作,之後加入高通。他畢業於加爾各答的 Jadavpur University,獲得電機工程學位,並且對數學充滿熱情。Santanu 擁有印度理工學院(IIT)海得拉巴分校的數據科學碩士學位。在空閒時間,他還參加 Kaggle 競賽,並在前 500 名中名列前茅。目前,他與妻子居住在班加羅爾。