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

Frequently asked questions


Q: What is the difference between machine learning and deep learning?

A: Deep learning is a specialized implementation of machine learning as an abstract concept. Machine learning algorithms are primarily the functions that draw lines through the data points in the case of supervised learning algorithms. The feature space is mapped as a multi-dimensional representation. This representation generalizes the datasets and can predict the value or the state of the actor for new environment states. Deep learning algorithms also model the real-world data within the context. However, they take a layered approach in creating the models. Each layer in the network specializes in a specific part of the input signal, starting from the high-level, more generic features in the initial layers, to the deeper and granular features in the subsequent layers toward the output layer. These networks are capable of training themselves based on some of the popular algorithms, such as backpropagation...