In the previous chapter, we established the fundamental theory of artificial neural networks (ANNs) and how they emulate human brain structure for generating output based on a set of inputs with the help of interconnected nodes. The nodes are arranged in three types of layers: input, hidden, and output. We understood the basic and mathematical concepts of how the input signal is carried through to the output layer and the iterative approach that ANNs take for training weights on neuron connections. Simple neural networks with one or two hidden layers can solve very rudimentary problems. However, in order to meaningfully utilize ANNs for real-world problems, which involve hundreds or thousands of input variables, involve more complex models, and require the models to store more information, we need more complex structures that are realized with large numbers...
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Book Overview & Buying
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Table Of Contents
Artificial Intelligence for Big Data
By :
Artificial Intelligence for Big Data
By:
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)
Preface
Free Chapter
Big Data and Artificial Intelligence Systems
Ontology for Big Data
Learning from Big Data
Neural Network for Big Data
Deep Big Data Analytics
Natural Language Processing
Fuzzy Systems
Genetic Programming
Swarm Intelligence
Reinforcement Learning
Cyber Security
Cognitive Computing
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