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

Gradient descent and backpropagation


Let's consider the following linear regression example where we have a set of training data. Based on the training data, we use forward propagation to model a straight line prediction function, h(x), as in the following diagram:

Figure 4.11: Forward propagation to model a straight line function

The difference between the actual and predicted value for an individual training sample contributes to the overall error for the prediction function. The goodness of fit for a neural network is defined with a cost function. It measures how well a neural network performed with respect to the training dataset when it modeled the training data.

As you can imagine, the cost function value in the case of the neural network is dependent on the weights on each neuron and the biases on each of the nodes. The cost function is a single value and it is representative of the overall neural network. The cost function takes the following form in a neural network:

C (W, Xr, Yr)

  • W...