All the models that we have analyzed until now have a common feature. Once the training process is completed, the weights are frozen and the output depends only on the input sample. Clearly, this is the expected behavior of a classifier, but there are many scenarios where a prediction must take into account the history of the input values. A time series is a classic example. Let's suppose that we need to predict the temperature for the next week. If we try to use only the last known x(t) value and an MLP trained to predict x(t+1), it's impossible to take into account temporal conditions like the season, the history of the season over the years, the position in the season, and so on. The regressor will be able to associate the output that yields the minimum average error, but in real-life situations, this isn't enough. The only reasonable way to solve this problem is to define a new architecture for the artificial neuron, to provide it with a memory. This concept is shown...
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.
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Current Title:
Mastering Machine Learning Algorithms
Table of Contents (22 chapters)
Title Page
Dedication
Packt Upsell
Contributors
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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Index
Customer Reviews