Book Image

Artificial Intelligence for Big Data

By : Anand Deshpande, Manish Kumar
Book Image

Artificial Intelligence for Big Data

By: Anand Deshpande, Manish Kumar

Overview of this book

In this age of big data, companies have larger amount of consumer data than ever before, far more than what the current technologies can ever hope to keep up with. However, Artificial Intelligence closes the gap by moving past human limitations in order to analyze data. With the help of Artificial Intelligence for big data, you will learn to use Machine Learning algorithms such as k-means, SVM, RBF, and regression to perform advanced data analysis. You will understand the current status of Machine and Deep Learning techniques to work on Genetic and Neuro-Fuzzy algorithms. In addition, you will explore how to develop Artificial Intelligence algorithms to learn from data, why they are necessary, and how they can help solve real-world problems. By the end of this book, you'll have learned how to implement various Artificial Intelligence algorithms for your big data systems and integrate them into your product offerings such as reinforcement learning, natural language processing, image recognition, genetic algorithms, and fuzzy logic systems.
Table of Contents (19 chapters)
Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
Index

Perceptron and linear models


Let's consider the example of a regression problem where we have two input variables and one output or dependent variable and illustrate the use of ANN for creating a model that can predict the value of the output variable for a set of input variables:

Figure 4.2 Sample training data

In this example, we have x1 and x2 as input variables and y as the output variable. The training data consists of five data points and the corresponding values of the dependent variable, y. The goal is to predict the value of y when x1 = 6 and x2 = 10. Any given continuous function can be implemented exactly by a three-layer neural network with n neurons in the input layer, 2n + 1 neurons in the hidden layer and m neurons in the hidden layer. Let's represent this with a simple neural network:

Figure 4.3 ANN notations

Component notations of the neural network

There is a standardized way in which the neural networks are denoted, as follows:

  • x1 and x2 are inputs (It is also possible to call...