This textbook presents a concise, accessible and engaging first introduction to deep learning, offering a wide range of connectionist models which represent the current state-of-the-art. The text explores the most popular algorithms and architectures in a simple and intuitive style, explaining the mathematical derivations in a step-by-step manner. The content coverage includes convolutional networks, LSTMs, Word2vec, RBMs, DBNs, neural Turing machines, memory networks and autoencoders. Numerous examples in working Python code are provided throughout the book, and the code is also supplied separately at an accompanying website.

Topics and features: introduces the fundamentals of machine learning, and the mathematical and computational prerequisites for deep learning; discusses feed-forward neural networks, and explores the modifications to these which can be applied to any neural network; examines convolutional neural networks, and the recurrent connections to a feed-forward neural network; describes the notion of distributed representations, the concept of the autoencoder, and the ideas behind language processing with deep learning; presents a brief history of artificial intelligence and neural networks, and reviews interesting open research problems in deep learning and connectionism.

This clearly written and lively primer on deep learning is essential reading for graduate and advanced undergraduate students of computer science, cognitive science and mathematics, as well as fields such as linguistics, logic, philosophy, and psychology.

这本教科书对深度学习进行了简明、易懂和引人入胜的首次介绍，提供了一系列代表当前最先进水平的连接主义模型。该书以简单直观的风格探讨了最流行的算法和架构，以循序渐进的方式解释了数学推导。内容包括卷积网络、LSTMs、Word2vec、RBMs、DBNs、神经图灵机、记忆网络和自动编码器。全书提供了大量的Python工作代码的例子，代码也在配套的网站上单独提供。

主题和特点：介绍了机器学习的基础知识，以及深度学习的数学和计算前提；讨论了前馈神经网络，并探讨了可应用于任何神经网络的修改；研究了卷积神经网络，以及前馈神经网络的递归连接；描述了分布式表征的概念、自动编码器的概念，以及用深度学习进行语言处理的想法；介绍了人工智能和神经网络的简要历史，并回顾了深度学习和连接主义的有趣开放研究问题。

这本书写清晰、生动的深度学习入门书是计算机科学、认知科学和数学以及语言学、逻辑学、哲学和心理学等领域的研究生和高级本科生的必备读物。