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It has been a long way since 1995, and many new techniques and important developments have taken place in the field of A.I. and more concretely, machine learning. Still, this book has aged very well, for two reasons: first, the fundamental techniques and concepts that every practitioner must understand and be able to make use of, like for example parametric techniques for density estimation (kNN), dimensionality reduction (PCA), mixture models, in addition to, of course, neural networks. Second, this book paves the way for moving on to modern techniques like deep energy models and deep belief networks with its last chapter on bayesian techniques.
The explanations are clear and amenable to read. Properties of and advances based on neural networks are presented in a principled way in the context of statistical pattern recognition. The exercises are wisely chosen to ensure the understanding of the presented results, and under what conditions they were derived.
But this book goes beyond theory, A chapter is devoted to optimization techniques, i.e. what algorithms are used to train neural networks in practice. After reading that chapter and going through the exercises you will have a good understanding of the conjugate gradients and LFGB.
The chapter on how to improve generalization, either by optimizing the structure of the network or by combining multiple classifiers is keep at a intuitive level, yet the concepts are well motivated and the few mathetical details help achieving a solid grasp of why do those ideas work. As in the rest of chapters, it is explained how to carry out it in practice, i.e. how I can proofcheck, if my classifier has become better. At the end of the chapter the reader is familiar with the concept of regularization (weight decay), cross validation and bagging.