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Machine Learning for Developers

You're reading from Machine Learning for Developers Uplift your regular applications with the power of statistics, analytics, and machine learning

Product type Paperback

Published in Oct 2017

Publisher Packt

ISBN-13 9781786469878

Length 270 pages

Edition 1st Edition

Languages

Python

Tools

Scikit-learn

Concepts

Machine Learning

Authors (2):

Bonnin

Hasan

View More author details

Table of Contents (17) Chapters

Title Page

Credits

Foreword

About the Author

About the Reviewers

www.PacktPub.com

Customer Feedback

Preface

1. Introduction - Machine Learning and Statistical Science FREE CHAPTER

2. The Learning Process

3. Clustering

4. Linear and Logistic Regression

5. Neural Networks

6. Convolutional Neural Networks

7. Recurrent Neural Networks

8. Recent Models and Developments

9. Software Installation and Configuration

Model implementation and results interpretation

No model is practical if it can't be used outside the training and test sets. This is when the model is deployed into production.

In this stage, we normally load all the model's operation and trained weights, wait for new unknown data, and when it arrives, we feed it through all the chained functions of the model, informing the outcomes of the output layer or operation via a web service, printing to standard output, and so on.

Then, we will have a final task - to interpret the results of the model in the real world to constantly check whether it works in the current conditions. In the case of generative models, the suitability of the predictions is easier to understand because the goal is normally the representation of a previously known entity.

Regression metrics

For regression metrics, a number of indicators are calculated to give a succinct idea of the fitness of the regressed model. Here is a list of the main metrics.

Mean absolute error

The...

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Authors (2)

Bonnin

Bonnin

Rodolfo Bonnin is a systems engineer and Ph.D. student at Universidad Tecnolgica Nacional, Argentina. He has also pursued parallel programming and image understanding postgraduate courses at Universitt Stuttgart, Germany. He has been doing research on high-performance computing since 2005 and began studying and implementing convolutional neural networks in 2008, writing a CPU- and GPU-supporting neural network feedforward stage. More recently he's been working in the field of fraud pattern detection with Neural Networks and is currently working on signal classification using machine learning techniques. He is also the author of Building Machine Learning Projects with Tensorflow and Machine Learning for Developers by Packt Publishing.

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Hasan

Hasan

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