Deep Learning

Kelleher, John D.

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商品描述

An accessible introduction to the artificial intelligence technology that enables computer vision, speech recognition, machine translation, and driverless cars.

Deep learning is an artificial intelligence technology that enables computer vision, speech recognition in mobile phones, machine translation, AI games, driverless cars, and other applications. When we use consumer products from Google, Microsoft, Facebook, Apple, or Baidu, we are often interacting with a deep learning system. In this volume in the MIT Press Essential Knowledge series, computer scientist John Kelleher offers an accessible and concise but comprehensive introduction to the fundamental technology at the heart of the artificial intelligence revolution.

Kelleher explains that deep learning enables data-driven decisions by identifying and extracting patterns from large datasets; its ability to learn from complex data makes deep learning ideally suited to take advantage of the rapid growth in big data and computational power. Kelleher also explains some of the basic concepts in deep learning, presents a history of advances in the field, and discusses the current state of the art. He describes the most important deep learning architectures, including autoencoders, recurrent neural networks, and long short-term networks, as well as such recent developments as Generative Adversarial Networks and capsule networks. He also provides a comprehensive (and comprehensible) introduction to the two fundamental algorithms in deep learning: gradient descent and backpropagation. Finally, Kelleher considers the future of deep learning--major trends, possible developments, and significant challenges.

商品描述(中文翻譯)

一本介紹人工智慧技術的書籍,該技術使得電腦視覺、語音識別、機器翻譯和無人駕駛汽車成為可能。

深度學習是一種人工智慧技術,它使得電腦視覺、手機語音識別、機器翻譯、人工智慧遊戲、無人駕駛汽車等應用成為可能。當我們使用Google、Microsoft、Facebook、Apple或Baidu等消費者產品時,我們通常正在與一個深度學習系統互動。在這本MIT Press Essential Knowledge系列的書中,計算機科學家John Kelleher提供了一個易於理解且簡潔但全面的介紹,該介紹涵蓋了人工智慧革命核心技術。

Kelleher解釋了深度學習通過從大型數據集中識別和提取模式來實現基於數據的決策;深度學習從複雜數據中學習的能力使其非常適合利用大數據和計算能力的快速增長。Kelleher還解釋了深度學習的一些基本概念,介紹了該領域的發展歷史,並討論了目前的最新技術。他描述了最重要的深度學習架構,包括自編碼器、循環神經網絡和長短期記憶網絡,以及生成對抗網絡和膠囊網絡等最新進展。他還全面且易於理解地介紹了深度學習中的兩個基本算法:梯度下降和反向傳播。最後,Kelleher考慮了深度學習的未來-主要趨勢、可能的發展和重大挑戰。