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Designing Machine Learning Systems with Python

Designing Machine Learning Systems with Python

By : David Julian
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Designing Machine Learning Systems with Python

Designing Machine Learning Systems with Python

2 (1)
By: David Julian

Overview of this book

Machine learning is one of the fastest growing trends in modern computing. It has applications in a wide range of fields, including economics, the natural sciences, web development, and business modeling. In order to harness the power of these systems, it is essential that the practitioner develops a solid understanding of the underlying design principles. There are many reasons why machine learning models may not give accurate results. By looking at these systems from a design perspective, we gain a deeper understanding of the underlying algorithms and the optimisational methods that are available. This book will give you a solid foundation in the machine learning design process, and enable you to build customised machine learning models to solve unique problems. You may already know about, or have worked with, some of the off-the-shelf machine learning models for solving common problems such as spam detection or movie classification, but to begin solving more complex problems, it is important to adapt these models to your own specific needs. This book will give you this understanding and more.
Table of Contents (11 chapters)
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1. Thinking in Machine Learning
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Index

Cost function


To fit the weights in a neural net for a given training set, we first need to define a cost function:

This is very similar to the cost function we used for logistic regression, except that now we are also summing over k output units. The triple summation used in the regularization term looks a bit complicated, but all it is really doing is summing over each of the terms in the parameter matrix, and using this to calculate the regularization. Note that the summation, i, l, and j start at 1, rather than 0; this is to reflect the fact that we do not apply regularization to the bias unit.

Minimizing the cost function

Now that we have cost function, we need to work out a way to minimize it. As with gradient descent, we need to compute the partial derivatives to calculate the slope of the cost function. This is done using the back propagation algorithm. It is called back propagation because we begin by calculating the error at the output layer, then calculating the error for each previous...

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