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.

深度学习是一种人工智能技术，能够实现计算机视觉、手机中的语音识别、机器翻译、人工智能游戏、无人驾驶汽车和其他应用。当我们使用谷歌、微软、Facebook、苹果或百度的消费产品时，我们往往是在与深度学习系统互动。在麻省理工学院出版社基本知识系列的这一卷中，计算机科学家约翰-凯莱赫对处于人工智能革命核心的基本技术进行了易懂、简明而全面的介绍。

凯莱赫解释说，深度学习通过从大型数据集中识别和提取模式来实现数据驱动的决策；其从复杂数据中学习的能力使深度学习非常适合利用快速增长的大数据和计算能力。Kelleher还解释了深度学习的一些基本概念，介绍了该领域的进展历史，并讨论了当前的技术状况。他描述了最重要的深度学习架构，包括自动编码器、递归神经网络和长短期网络，以及诸如生成对抗网络和胶囊网络等最新发展。他还对深度学习中的两种基本算法进行了全面的（和可理解的）介绍：梯度下降和反向传播。最后，Kelleher考虑了深度学习的未来--主要趋势、可能的发展和重大挑战。