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.
