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Artificial Intelligence for Big Data

Artificial Intelligence for Big Data

By : Deshpande, Kumar
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Artificial Intelligence for Big Data

Artificial Intelligence for Big Data

5 (2)
By: Deshpande, 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 (14 chapters)
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Practical approach to implementing neural net architectures


While the deep neural networks are good at generalizing the training data with multi-layered iteratively-generated models, the practical application of these algorithms and theory requires careful consideration of various approaches. This section introduces general guiding principles for using the deep neural networks in practical scenarios. At a high level, we can follow a cyclic process for deployment and the use of deep neural networks, as depicted in this diagram:

We explain the preceding diagram as follows:

  • Define and realign the goals: This is applicable not only to the deep neural networks but in general use of the machine learning algorithms. The use-case-specific goals related to the choice error metric and threshold target value for the metric need to be set as the first step. The goal around the error metric defines the actions in the subsequent stages of architectural design and various design choices. It is unrealistic...
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Artificial Intelligence for Big Data
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