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

Recurrent neural networks


So far, we have seen the ANNs where the input signals are propagated to the output layer in the forward pass and the weights are optimized in a recursive manner in order to train the model for generalizing the new input data based on the training set provided as input.

A special case real-life problem is optimizing the ANN for training sequences of data, for example, text, speech, or any other form of audio input. In simple terms, when the output of one forward propagation is fed as input for the next iteration of training, the network topology is called a recurrent neural network (RNN).

The need for RNNs

In the case of the feed-forward networks, we consider independent sets of inputs. In the case of image recognition problems, we have input images that are independent of each other in terms of the input dataset. In this case, we consider the pixel matrix for the input image. The input data for one image does not influence the input for the next image that the ANN...