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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
10
Index

Gradient checking


Back propagation, and neural nets in general, are a little difficult to conceptualize. So, it is often not easy to understand how changing any of the model (hyper) parameters will affect the outcome. Furthermore, with different implementations, it is possible to get results that indicate that an algorithm is working correctly, that is, the cost function is decreasing on each level of gradient descent. However, as with any complicated software, there can be hidden bugs that might only manifest themselves under very specific conditions. A way to help eliminate these is through a procedure called gradient checking. This is a numerical way of approximating gradients, and we can understand this intuitively by examining the following diagram:

The derivative of J(w), with respect to w, can be approximated as follows:

The preceding formula approximates the derivative when the parameter is a single value. We need to evaluate these derivatives on a cost function, where the weights...

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