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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

Logistic units


As a starting point, we use the idea of a logistic unit over the simplified model of a neuron. It consists of a set of inputs and outputs and an activation function. This activation function is essentially performing a calculation on the set of inputs, and subsequently giving an output. Here, we set the activation function to the sigmoid that we used for logistic regression in the previous chapter:

We have Two input units, x1 and x2 and a bias unit, x0, that is set to one. These are fed into a hypothesis function that uses the sigmoid logistic function and a weight vector, w, which parameterizes the hypothesis function. The feature vector, consisting of binary values, and the parameter vector for the preceding example consist of the following:

To see how we can get this to perform logical functions, let's give the model some weights. We can write this as a function of the sigmoid, g, and our weights. To get started, we are just going to choose some weights. We will learn shortly...

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