English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 1h 46m | 312 MB
Solve Machine Learning problems with C++
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.
In this course, you will learn about the popular Machine Learning libraries and see how you can choose the perfect library for a selected problem.
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++
Table of Contents
Knowing Libraries and Open Datasets
1 The Course Overview
2 Popular C++ ML Libraries Overview
3 Available Datasets
4 Datasets for Different Scenarios
Implementation with Shark
5 Environment Setup_ Brief Library Characteristics of Shark
6 Implementing Linear Regression with Shark
7 Library Specific Features and Utilities of Shark
Implementation with MLPACK
8 Environment Setup_Brief Library Characteristics of MLPACK
9 Implementing K-Means with MLPACK
10 Library Specific Features and Utilities of MLPACK
Implementation with Dlib
11 Environment Setup_Brief Library Characteristics of Dlib
12 Implementing K-Means with Dlib
13 Library Specific Features and Utilities of Dlib
Implementation with OpenCV
14 Environment Setup_Brief Library Characteristics of OpenCV
15 Implementing Face Detection with OpenCV
16 Library Specific Features and Utilities of OpenCV
Recap
17 Comparison of Libraries
18 Which Library Do I Choose
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