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Applications associated with many different areas like recommender systems, machine translation, captioning, image classification, reinforcement-learning based gaming, and text analytics are covered. The following aspects are covered: The basics of neural networks: Chapters 1 and 2 discuss the basics of neural network design and also the fundamentals of training them. The simulation of various machine learning models with neural networks is provided. Examples include least-squares regression, SVMs, logistic regression, Widrow-Hoff learning, singular value decomposition, and recommender systems. Recent models like word2vec are also explored, together with their connections with traditional matrix factorization. Exploring the interface between machine learning and neural networks is important because it provides a deeper understanding of how neural networks generalize known machine learning methods, and the cases in which neural networks have advantages over traditional machine learning. Challenges in training neural networks: Although Chapters 1 and 2 provide an overview of the training methods for neural networks, a more detailed understanding of the training challenges is provided in Chapters 3 and 4. In particular, issues related to network depth and also overfitting are discussed. Chapter 5 presents a classical architecture, referred to as radial-basis function networks. Even though this architecture is no longer used frequently, it is important because it represents a direct generalization of the kernel support-vector machine. Advanced architectures and applications: A lot of the success in neural network design is a result of the specialized architectures for various domains and applications. Examples of such specialized architectures include recurrent neural networks and convolutional neural networks. Since the specialized architectures form the key to the understanding of neural network performance in various domains, most of the book will be devoted to this setting. Several advanced topics like deep reinforcement learning, neural Turing mechanisms, and generative adversarial networks are discussed. Some of the ``forgotten'' architectures like RBF networks and Kohonen self-organizing maps are included because of their potential in many applications. The book is written for graduate students, researchers, and practitioners. The book does require knowledge of probability and linear algebra. Furthermore basic knowledge of machine learning is helpful. 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 give the reader a feel for the technology. The book is available in both hardcopy (hardcover) and electronic versions. The hardcover is available at all the usual channels (e.g, Amazon, Barnes and Noble etc.), in Kindle format, and also directly from Springer in hardcopy and pdf format. The good thing about Springer is that electronic versions are often widely accessible at no cost to subscribing institutions, which is particularly convenient for students. My understanding is that a very large fraction of universities in North America, Europe, Australia, and New Zealand are subscribers, and a rapidly increasing number of universities in Asia are also subscribing. The electronic version is available at the following Springerlink pointer . For subscribing institutions click from a computer directly connected to your institution network to download the book for free. Springer uses the domain name of your computer to regulate access. To be eligible, your institution must subscribe to "e-book package english (Computer Science)" or "e-book package english (full collection)". If your institution is eligible, you will see a (free) `Download Book' button. Otherwise you will see a (paid) `Get Access' button. Sometimes you may be able to download it from your library e-collection, even when it is not Web-accessible from your institution. For those who prefer desk copies rather than electronic books, there are some very cost-effective methods to obtain a paperback MyCopy edition for $25 or less (subscribing institutions only). 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Contact Information: Charu Aggarwal IBM T. J. Watson Research Center, 1101 Kitchawan Rd, Yorktown, NY 10598 Email: charu (at) us (dot) ibm (dot) com In case you have sent me an email at my earlier address with domain name watson.ibm.com, it is likely that I have not received it.
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