As explained in the previous section, one of the most difficult problems with standard GANs is caused by the loss function based on the Jensen-Shannon divergence, whose value becomes constant when two distributions have disjointed supports. This situation is quite common with high-dimensional, semantically structured datasets. For example, images are constrained to having particular features in order to represent a specific subject (this is a consequence of the manifold assumption discussed in Chapter 2, Introduction to Semi-Supervised Learning). The initial generator distribution is very unlikely to overlap a true dataset, and in many cases, they are also very far from each other. This condition increases the risk of learning a wrong representation (a problem known as mode collapse), even when the discriminator is able to distinguish between true and generated...
Mastering Machine Learning Algorithms
Mastering Machine Learning Algorithms
Overview of this book
Machine learning is a subset of AI that aims to make modern-day computer systems smarter and more intelligent. The real power of machine learning resides in its algorithms, which make even the most difficult things capable of being handled by machines. However, with the advancement in the technology and requirements of data, machines will have to be smarter than they are today to meet the overwhelming data needs; mastering these algorithms and using them optimally is the need of the hour.
Mastering Machine Learning Algorithms is your complete guide to quickly getting to grips with popular machine learning algorithms. You will be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and will learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this book will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries such as scikit-learn v0.19.1. You will also learn how to use Keras and TensorFlow 1.x to train effective neural networks.
If you are looking for a single resource to study, implement, and solve end-to-end machine learning problems and use-cases, this is the book you need.
Table of Contents (17 chapters)
Preface
Free Chapter
Machine Learning Model Fundamentals
Introduction to Semi-Supervised Learning
Graph-Based Semi-Supervised Learning
Bayesian Networks and Hidden Markov Models
EM Algorithm and Applications
Hebbian Learning and Self-Organizing Maps
Clustering Algorithms
Ensemble Learning
Neural Networks for Machine Learning
Advanced Neural Models
Autoencoders
Generative Adversarial Networks
Deep Belief Networks
Introduction to Reinforcement Learning
Advanced Policy Estimation Algorithms
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