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Artificial Intelligence for Big Data

You're reading from   Artificial Intelligence for Big Data Complete guide to automating Big Data solutions using Artificial Intelligence techniques

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Product type Paperback
Published in May 2018
Publisher Packt
ISBN-13 9781788472173
Length 384 pages
Edition 1st Edition
Languages
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Authors (2):
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Anand Deshpande Anand Deshpande
Author Profile Icon Anand Deshpande
Anand Deshpande
Manish Kumar Manish Kumar
Author Profile Icon Manish Kumar
Manish Kumar
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Toc

Table of Contents (14) Chapters Close

Preface 1. Big Data and Artificial Intelligence Systems 2. Ontology for Big Data FREE CHAPTER 3. Learning from Big Data 4. Neural Network for Big Data 5. Deep Big Data Analytics 6. Natural Language Processing 7. Fuzzy Systems 8. Genetic Programming 9. Swarm Intelligence 10. Reinforcement Learning 11. Cyber Security 12. Cognitive Computing 13. Other Books You May Enjoy

Deep learning basics and the building blocks


In the previous chapters, we established the fact that the machine learning algorithms generalize the input data into a hypothesis that fits the data so that the output, based on the new values, can be predicted accurately by the model. The accuracy of the model is a function of the amount of the input data along with variation in the values of the independent variables. The more data and variety, the more computation power we require to generate and execute the models. The distributed computing frameworks (Hadoop, Spark, and so on) work very well with the large volumes of data with variety. The same principles apply to ANNs.

The more input data we have along with variations, the more accurate the models can be generated, which requires more storage and computation power. Since the computation power and storage is available with the development of the big data analytics platforms (in-premise as well as on the cloud), it is possible to experiment...

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