Book Image

Java: Data Science Made Easy

By : Richard M. Reese, Jennifer L. Reese, Alexey Grigorev
Book Image

Java: Data Science Made Easy

By: Richard M. Reese, Jennifer L. Reese, Alexey Grigorev

Overview of this book

Data science is concerned with extracting knowledge and insights from a wide variety of data sources to analyse patterns or predict future behaviour. It draws from a wide array of disciplines including statistics, computer science, mathematics, machine learning, and data mining. In this course, we cover the basic as well as advanced data science concepts and how they are implemented using the popular Java tools and libraries.The course starts with an introduction of data science, followed by the basic data science tasks of data collection, data cleaning, data analysis, and data visualization. This is followed by a discussion of statistical techniques and more advanced topics including machine learning, neural networks, and deep learning. You will examine the major categories of data analysis including text, visual, and audio data, followed by a discussion of resources that support parallel implementation. Throughout this course, the chapters will illustrate a challenging data science problem, and then go on to present a comprehensive, Java-based solution to tackle that problem. You will cover a wide range of topics – from classification and regression, to dimensionality reduction and clustering, deep learning and working with Big Data. Finally, you will see the different ways to deploy the model and evaluate it in production settings. By the end of this course, you will be up and running with various facets of data science using Java, in no time at all. This course contains premium content from two of our recently published popular titles: - Java for Data Science - Mastering Java for Data Science
Table of Contents (29 chapters)
Title Page
Credits
Preface
Free Chapter
1
Module 1
15
Module 2
26
Bibliography

Training a neural network


There are three basic training approaches:

  • Supervised learning - With supervised learning the model is trained with data that matches input sets to output values
  • Unsupervised learning - In unsupervised learning, the data does not contain results, but the model is expected to determine relationships on its own
  • Reinforcement learning - Similar to supervised learning, but a reward is provided for good results

These datasets differ in the information they contain. Supervised and reinforcement learning contain correct output for a set of input. The unsupervised learning does not contain correct results.

A neural network learns (at least with supervised learning) by feeding an input into a network and comparing the results, using the activation function, to the expected outcome. If they match, then the network has been trained correctly. If they don't match then the network is modified.

When we modify the weights we need to be careful not to change them too drastically. If...