Table of Contents
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This book covers both classical and modern models in deep learning. 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 mac...
Machine learning techniques provide cost-effective alternatives to traditional methods for extracting underlying relationships between information and data and for predicting future events by processing existing information to train models. Efficient Learning Machines explores the major topics of machine learning, including knowledge discovery, cla...
This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural ...
A deep learning is of great interest these days, the crucial necessity for rapid and optimized implementation of the algorithms and designing architectures is the software environment. TensorFlow is designed to facilitate this goal. The strong advantage of TensorFlow is it flexibility is designing highly modular model which also can be a disadvanta...
Neural networks are a powerful tool for developers, but harnessing them can be a challenge. With Keras Succinctly, author James McCaffrey introduces Keras, an open-source, neural network library designed specifically to make working with backend neural network tools easier....
This much anticipated and thoroughly revis...