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Mastering Machine Learning Algorithms

Mastering Machine Learning Algorithms

3.4 (5)
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Mastering Machine Learning Algorithms

Mastering Machine Learning Algorithms

3.4 (5)

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)
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13
Deep Belief Networks

Neural Networks for Machine Learning

This chapter is the introduction to the world of deep learning, whose methods make it possible to achieve the state-of-the-art performance in many classification and regression fields often considered extremely difficult to manage (such as image segmentation, automatic translation, voice synthesis, and so on). The goal is to provide the reader with the basic instruments to understand the structure of a fully connected neural network and model it using the Python tool Keras (employing all the modern techniques to speed the training process and prevent overfitting).

In particular, the topics covered in the chapter are as follows:

  • The structure of a basic artificial neuron
  • Perceptrons, linear classifiers, and their limitations
  • Multilayer perceptrons with the most important activation functions (such as ReLU)
  • Back-propagation algorithms based...
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