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

Summary


In this chapter, we took our understanding of the ANNs further, to the deep neural networks that contain more than one, and up to hundreds and thousands of, hidden layers. The learning based on these deep neural networks is called deep learning. Deep learning is evolving as one of the most popular algorithms for solving some of the extremely complex problems within a stochastic environment. We have established the fundamental theory behind the working of deep neural networks and looked at the building blocks of gradient based-learning, backpropagation, nonlinearities, and the regularization technique- dropout. We have also reviewed some of the specialized neural network architecture's CNNs and RNNs.

We have also studied practical approaches for building data preparation pipelines and looked at the examples of applying regularization using the Weka library along with the DataVec library. We have studied some practical approaches for implementing neural network architectures. We have...