This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls?

The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Applications associated with many different areas like recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics are covered.

The chapters of this book span three categories:

The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. These methods are studied together with recent feature engineering methods like word2vec.

Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 3 and 4. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines.

Advanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks. Several advanced topics like deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 9 and 10.

The book is written for graduate students, researchers, and practitioners. Numerous exercises are available along with a solution manual to aid in classroom teaching. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.

本书涵盖了深度学习中的经典和现代模型。主要关注的是深度学习的理论和算法。神经网络的理论和算法对于理解重要的概念尤为重要，这样就可以理解不同应用中神经架构的重要设计理念。为什么神经网络能工作？什么时候它们比现成的机器学习模型效果更好？什么时候深度是有用的？为什么训练神经网络如此困难？有哪些隐患？

本书还丰富地讨论了不同的应用，以便让从业者了解神经架构是如何为不同类型的问题设计的。书中涵盖了与许多不同领域相关的应用，如推荐系统、机器翻译、图像说明、图像分类、基于强化学习的游戏和文本分析。

本书的章节横跨三个类别。

神经网络的基础知识。许多传统的机器学习模型可以被理解为神经网络的特例。前两章的重点是理解传统机器学习和神经网络之间的关系。支持向量机、线性/逻辑回归、奇异值分解、矩阵分解和推荐系统被证明是神经网络的特例。这些方法与最近的特征工程方法如word2vec一起被研究。

神经网络的基本原理。第3章和第4章对训练和正则化进行了详细讨论。第5章和第6章介绍了径向基准函数（RBF）网络和受限玻尔兹曼机。

神经网络的高级课题。第7章和第8章讨论递归神经网络和卷积神经网络。第9章和第10章介绍了几个高级课题，如深度强化学习、神经图灵机、Kohonen自组织图和生成对抗网络。

本书是为研究生、研究人员和从业人员编写的。书中提供了大量的练习题和解决手册，以帮助课堂教学。在可能的情况下，强调以应用为中心的观点，以提供对每一类技术的实际用途的理解。