Introduction-to-Machine-Learning-C-Libraries

Introduction to Machine Learning C++ Libraries, published by Packt

An Introduction into Machine Learning C++ Libraries [Video]

This is the code repository for An Introduction into Machine Learning C++ Libraries [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.

About the Video Course

Being able to perform machine learning in C++ will make you a very desirable hiring target. Not that you wouldn’t be if you used any other language but, the truth is that machine learning in C++ is a great combination that is likely to give you access to very interesting positions!

In this course, we focus on the practical part of machine learning—employing different C++ libraries. Several popular machine learning libraries currently exist—we’ll review them and you’ll become familiar with four of them. We use examples of standard machine learning algorithms implemented through the libraries. The course ends with hints that will help you to choose a library depending on the requirements of the situation.

Taking this course will not only help you build a familiarity with existing machine learning libraries, but also solve complex machine learning problems.

What You Will Learn

You will be introduced to four major machine learning libraries
Go through the installation and environment setup for each of the four libraries
Walk through a simple machine learning example for each library to familiarize yourself
Compare and contrast the libraries and look at their suitability for certain situations.
Understand the Popular C++ ML libraries and when to use them
Know how to install Shark, Dlib, Mlpack, and OpenCV
For each of the four libraries, we will explore the characteristics
Know how to solve Machine Learning problems with C++

Instructions and Navigation

Assumed Knowledge

To fully benefit from the coverage included in this course, you will need:
This course is for users with C++ experience who want an introduction to machine learning libraries at a high level. We won’t be going into any mathematical detail, so an understanding of the mathematics behind the algorithm is not essential, however, it will certainly be an advantage.

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