Book Image

Machine Learning Algorithms

Book Image

Machine Learning Algorithms

Overview of this book

In this book, you will learn all the important machine learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. The algorithms that are covered in this book are linear regression, logistic regression, SVM, naïve Bayes, k-means, random forest, TensorFlow and feature engineering. In this book, you will how to use these algorithms to resolve your problems, and how they work. This book will also introduce you to natural language processing and recommendation systems, which help you to run multiple algorithms simultaneously. On completion of the book, you will know how to pick the right machine learning algorithm for clustering, classification, or regression for your problem
Table of Contents (22 chapters)
Title Page
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface

Chapter 14. A Brief Introduction to Deep Learning and TensorFlow

In this chapter, we're going to briefly introduce deep learning with some examples based on TensorFlow. This topic is quite complex and needs dedicated books; however, our goal is to allow the reader to understand some basic concepts that can be useful before starting a complete course. In the first section, we're presenting the structure of artificial neural networks and how they can be transformed in a complex computational graph with several different layers. In the second one, instead, we're going to introduce the basic concepts concerning TensorFlow and we'll show some examples based on algorithms already discussed in previous chapters. In the last section, we briefly present Keras, a high-level deep learning framework and we build an example of image classification using a convolutional neural network.