Advanced Deep Learning with TensorFlow 2 and Keras - Second Edition
暫譯: 進階深度學習：使用 TensorFlow 2 和 Keras（第二版）
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
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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)
《進階深度學習：使用 TensorFlow 2 和 Keras（第二版）》是一本完全更新的暢銷指南，介紹當前可用的進階深度學習技術。本書針對 TensorFlow 2.x 進行了修訂，並引入了無監督學習（使用互信息）、物體檢測（SSD）和語義分割（FCN 和 PSPNet）等新章節，進一步幫助您創建自己的尖端 AI 專案。
本書使用 Keras 作為開源深度學習庫，包含實作專案，展示如何使用最新技術創建更有效的 AI。
本書首先概述了多層感知器（MLPs）、卷積神經網絡（CNNs）和遞迴神經網絡（RNNs），然後介紹更尖端的技術，探索深度神經網絡架構，包括 ResNet 和 DenseNet，以及如何創建自編碼器。接著，您將學習生成對抗網絡（GANs），以及它們如何解鎖 AI 性能的新層次。
接下來，您將發現變分自編碼器（VAE）的實作方式，以及 GANs 和 VAEs 如何具備生成數據的能力，這些數據對人類來說可能極具說服力。您還將學習實作深度強化學習（DRL），例如深度 Q 學習和策略梯度方法，這些對於許多現代 AI 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校區電機與電子工程學院的副教授。他擔任 Dado 和 Maria Banatao 人工智慧研究所教授職位，並因其在人工智慧增強的四足機器人方面的研究獲得新加坡國立大學的碩士學位。他在澳洲國立大學完成博士學位，專注於人機互動中的主動注視追蹤技術。他目前的研究工作集中在人工智慧和計算機視覺領域。
