Book Image

Statistics for Machine Learning

By : Pratap Dangeti
Book Image

Statistics for Machine Learning

By: Pratap Dangeti

Overview of this book

Complex statistics in machine learning worry a lot of developers. Knowing statistics helps you build strong machine learning models that are optimized for a given problem statement. This book will teach you all it takes to perform the complex statistical computations that are required for machine learning. You will gain information on the statistics behind supervised learning, unsupervised learning, reinforcement learning, and more. You will see real-world examples that discuss the statistical side of machine learning and familiarize yourself with it. You will come across programs for performing tasks such as modeling, parameter fitting, regression, classification, density collection, working with vectors, matrices, and more. By the end of the book, you will have mastered the statistics required for machine learning and will be able to apply your new skills to any sort of industry problem.
Table of Contents (16 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Introduction to deep learning


Deep learning is a class of machine learning algorithms which utilizes neural networks for building models to solve both supervised and unsupervised problems on structured and unstructured datasets such as images, videos, NLP, voice processing, and so on:

Deep neural network/deep architecture consists of multiple hidden layers of units between input and output layers. Each layer is fully connected with the subsequent layer. The output of each artificial neuron in a layer is an input to every artificial neuron in the next layer towards the output:

With the more number of hidden layers are being added to the neural network, more complex decision boundaries are being created to classify different categories. Example of complex decision boundary can be seen in the following graph:

Solving methodology

Backpropagation is used to solve deep layers by calculating the error of the network at output units and propagate back through layers to update the weights to reduce error...