Advanced Deep Learning with TensorFlow 2 and Keras - Second Edition
Atienza, Rowel
- 出版商: Packt Publishing
- 出版日期: 2020-02-28
- 售價: $1,500
- 貴賓價: 9.5 折 $1,425
- 語言: 英文
- 頁數: 512
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1838821651
- ISBN-13: 9781838821654
-
相關分類:
DeepLearning、TensorFlow
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商品描述
Advanced Deep Learning with TensorFlow 2 and Keras, Second Edition is a completely updated edition of the bestselling guide to the advanced deep learning techniques available today. Revised for TensorFlow 2.x, this edition introduces you to the practical side of deep learning with new chapters on unsupervised learning using mutual information, object detection (SSD), and semantic segmentation (FCN and PSPNet), further allowing you to create your own cutting-edge AI projects.
Using Keras as an open-source deep learning library, the book features hands-on projects that show you how to create more effective AI with the most up-to-date techniques.
Starting with an overview of multi-layer perceptrons (MLPs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), the book then introduces more cutting-edge techniques as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You will then learn about GANs, and how they can unlock new levels of AI performance.
Next, you’ll discover how a variational autoencoder (VAE) is implemented, and how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans. You'll also learn to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI.
商品描述(中文翻譯)
《深度學習進階：使用TensorFlow 2和Keras，第二版》是一本全面更新的暢銷指南，介紹了當今可用的先進深度學習技術。這本書經過TensorFlow 2.x的修訂，引入了關於使用互信息進行無監督學習、物體檢測（SSD）和語義分割（FCN和PSPNet）的新章節，讓您能夠創建尖端的人工智能項目。
使用Keras作為開源深度學習庫，本書提供了實踐項目，展示了如何使用最新技術創建更有效的人工智能。
從多層感知器（MLPs）、卷積神經網絡（CNNs）和循環神經網絡（RNNs）的概述開始，本書隨後介紹了更先進的技術，包括ResNet和DenseNet等深度神經網絡架構，以及如何創建自編碼器。然後，您將學習關於生成對抗網絡（GANs）以及它們如何提升人工智能性能的新水平。
接下來，您將了解如何實現變分自編碼器（VAE），以及GANs和VAEs如何具有生成能力，能夠合成對人類極具說服力的數據。您還將學習實現深度強化學習（DRL），例如深度Q學習和策略梯度方法，這對於許多現代人工智能的成果至關重要。
作者簡介
Rowel Atienza is an Associate Professor at the Electrical and Electronics Engineering Institute of the University of the Philippines, Diliman. He holds the Dado and Maria Banatao Institute Professorial Chair in Artificial Intelligence and received his MEng from the National University of Singapore for his work on an AI-enhanced four-legged robot. He finished his Ph.D. at The Australian National University for his contribution in the field of active gaze tracking for human-robot interaction. His current research work focuses on AI and computer vision.
作者簡介(中文翻譯)
Rowel Atienza是菲律賓大學迪利曼分校電機工程學院的副教授。他擁有Dado和Maria Banatao人工智慧研究所教授講座，並在新加坡國立大學獲得碩士學位，研究AI增強的四足機器人。他在澳大利亞國立大學完成博士學位，研究領域為人機交互的主動凝視追蹤。他目前的研究工作集中在人工智慧和計算機視覺領域。
