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Machine Learning for Developers

You're reading from  Machine Learning for Developers

Product type Book
Published in Oct 2017
Publisher Packt
ISBN-13 9781786469878
Pages 270 pages
Edition 1st Edition
Languages
Concepts
Authors (2):
Rodolfo Bonnin Rodolfo Bonnin
Profile icon Rodolfo Bonnin
LinkedIn
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.
See other products by Rodolfo Bonnin
Md Mahmudul Hasan Md Mahmudul Hasan
Profile icon Md Mahmudul Hasan
LinkedIn
See other products by Md Mahmudul Hasan
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Table of Contents (10) Chapters

Preface Introduction - Machine Learning and Statistical Science The Learning Process Clustering Linear and Logistic Regression Neural Networks Convolutional Neural Networks Recurrent Neural Networks Recent Models and Developments Software Installation and Configuration

Software Installation and Configuration

So, it's time to begin our farewell! In this chapter, we will detail all the instructions for building our development environment.

In this chapter, you will learn about:

  • Anaconda and pip environment installation on Linux
  • Anaconda and easy install environment installation on macOS
  • Anaconda environment installation on Windows

So, let's start with the step-by-step instructions, starting with Linux.

Linux installation

Linux is, in our view, the most flexible platform on which you can work for machine learning projects. As you probably know, there is a really large number of alternatives in the Linux realm, and they all have their own particular package management.

Given that writing instructions for working on all these distributions would take a large number of pages, we will settle on instructions for the Ubuntu 16.04 distribution.

Ubuntu is undoubtedly the most widespread Linux distribution, and in the particular case of Ubuntu 16.04 it is an LTS version, or long term support. This means that the base software we will run in this book will have support until the year 2021.

You will find more information about the meaning of LTS at https://wiki.ubuntu.com/LTS.

Regarding the feasibility of Ubuntu as a scientific computing distribution, even if it is considered by many to...

macOS X environment installation

Now it's the turn of macOS X installation. The installation process is very similar to Linux, and is based on the OS X High Sierra edition.

The installation requires sudo privileges for the installing user.

Anaconda installation

Anaconda can be installed via a graphical installer or a console-based one. In this section, we will cover the graphical installer. First, we will download the installer package from https://www.anaconda.com/download/ and choose the 64-bit package:

Once we have downloaded the installer package, we execute the installer, and we are presented with the step-by-step GUI:

Then, we choose the installation location (take into account that the whole package needs almost...

Windows installation

Windows is a platform on which Python can run without problems. In this section, we will cover the installation of Anaconda on the Windows platform.

Anaconda Windows installation

The process to install Anaconda is pretty similar to the macOS one, because of the graphic installer. Let's start by downloading the installer package from https://www.anaconda.com/download/ and choosing the 64-bit package:

After downloading the installer, accept the license agreement, and go to the next step:

Then, you can choose to install the platform for your current user, or for all users:

Then, you choose the installation directory for the whole installation. Remember, this will take close to 2 GB of disk to...

Summary

Congratulations! You have reached the end of this practical summary of the basic principles of machine learning. In this last chapter, we have covered a lot of ways to help you to build your machine learning computing environment.

We want to take the opportunity to sincerely thank you for your attentive reading, and we hope you have found the material presented interesting and engaging. Hopefully you are now ready to begin tackling new challenging problems, with the help of the tools we have presented, as well as new tools being developed all the time, and with the knowledge we have striven to provide.

For us, it has been a blast to write this book and search for the best ways to help you understand the concepts in a practical manner. Don't hesitate to write with questions, suggestions, or bug reports to the channels made available by the publisher.

Best regards,...

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Authors (2)

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Profile icon Rodolfo Bonnin
LinkedIn
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.
Read more
See other products by Rodolfo Bonnin
Profile icon Md Mahmudul Hasan
LinkedIn
See other products by Md Mahmudul Hasan
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