by Tom Tom | Released March 2018 | ISBN: 9781788477956

Solve Machine Learning problems with C++About This VideoDevelop an awareness and familiarity of existing machine learning libraries.Be able to choose an appropriate library depending on the task requirements.Solve complex Machine Learning problems with popular C++ LibrariesIn Detaileing 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.All the code and supporting files for this course are available on Github at https://github.com/PacktPublishing/Introduction-to-Machine-Learning-C-Libraries Show and hide more

1. Chapter 1 : Knowing Libraries and Open Datasets
   • The Course Overview 00:04:05
   • Popular C++ ML Libraries Overview 00:06:25
   • Available Datasets 00:05:36
   • Datasets for Different Scenarios 00:06:56
2. Chapter 2 : Implementation with Shark
   • Environment Setup/ Brief Library Characteristics of Shark 00:04:07
   • Implementing Linear Regression with Shark 00:14:22
   • Library Specific Features and Utilities of Shark 00:04:04
3. Chapter 3 : Implementation with MLPACK
   • Environment Setup/Brief Library Characteristics of MLPACK 00:04:35
   • Implementing K-Means with MLPACK 00:14:37
   • Library Specific Features and Utilities of MLPACK 00:03:11
4. Chapter 4 : Implementation with Dlib
   • Environment Setup/Brief Library Characteristics of Dlib 00:01:47
   • Implementing K-Means with Dlib 00:07:49
   • Library Specific Features and Utilities of Dlib 00:04:16
5. Chapter 5 : Implementation with OpenCV
   • Environment Setup/Brief Library Characteristics of OpenCV 00:04:18
   • Implementing Face Detection with OpenCV 00:08:05
   • Library Specific Features and Utilities of OpenCV 00:03:28
6. Chapter 6 : Recap
   • Comparison of Libraries 00:04:05
   • Which Library Do I Choose? 00:04:33
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