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David Julian - Designing Machine Learning Systems with Python [2016, PDF/EPUB/MOBI, ENG]

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Post 07-Jul-2016 21:00

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Designing Machine Learning Systems with Python
Год издания: 2016
Автор: David Julian
Жанр или тематика: Программирование
Издательство: Packt Publishing
ISBN: 9781785882951
Язык: Английский
Формат: PDF/EPUB/MOBI
Качество: Издательский макет или текст (eBook)
Интерактивное оглавление: Да
Количество страниц: 232
Описание: 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.
What You Will Learn
- Gain an understanding of the machine learning design process
- Optimize the error function of your machine learning system
- Understand the common programming patterns used in machine learning
- Discover optimizing techniques that will help you get the most from your data
- Find out how to design models uniquely suited to your task

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Table of Contents
1: Thinking in Machine Learning
2: Tools and Techniques
3: Turning Data into Information
4: Models – Learning from Information
5: Linear Models
6: Neural Networks
7: Features – How Algorithms See the World
8: Learning with Ensembles
9: Design Strategies and Case Studies
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