Kirill Kolodiazhnyi - Hands-On Machine Learning with C++

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Kirill Kolodiazhnyi is a seasoned software engineer with expertise in custom software development. He has several years of experience in building machine learning models and data products using C++. He holds a bachelor's degree in computer science from Kharkiv National University of Radio Electronics. He currently works in Kharkiv, Ukraine, where he lives with his wife and daughter.

Davor Lozi is a university lecturer living in Croatia. He likes working on algorithmic/mathematical problems, and reviewing books for Packt makes him read new IT books. He has also worked on Data Analysis with R Second Edition; Mastering Predictive Analytics with R, Second Edition; R Data Analysis Cookbook, Second Edition; R Deep Learning Projects; Mastering Linux Network Administration; R Machine Learning Projects; Learning Ext JS, Fourth Edition; and R Statistics Cookbook. Davor is a meme master and an Uno master, and he likes cats.

Dr. Ashwin Nanjappa works at NVIDIA on deep learning inference acceleration on GPUs. He has a Ph.D. from the National University of Singapore, where he invented the fastest 3D Delaunay computational geometry algorithms for GPUs. He was a postdoctoral research fellow at the BioInformatics Institute (Singapore), inventing machine learning algorithms for hand and rodent pose estimation using depth cameras. He also worked at Visenze (Singapore) developing computer vision deep learning models for the largest e-commerce portals in the world. He is a published author of two books: Caffe2 Quick Start Guide and Instant GLEW.

Ryan Riley has been involved in the futures and derivatives industry for almost 20 years. He received a bachelor's degree and a master's degree from DePaul University in applied statistics. Doing his coursework in math meant that he had to teach himself how to program, forcing him to read more technical books on programming than he would otherwise have done. Ryan has worked with numerous AI libraries in various languages and is currently using the Caffe2 C++ library to develop and implement futures and derivatives trading strategies at PNT Financial.

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Machine learning (ML) is a popular approach to solve different kinds of problems. ML allows you to deal with various tasks without knowing a direct algorithm to solve them. The key feature of ML algorithms is their ability to learn solutions by using a set of training samples, or even without them. Nowadays, ML is a widespread approach used in various areas of industry. Examples of areas where ML outperforms classical direct algorithms include computer vision, natural language processing, and recommender systems.

This book is a handy guide to help you learn the fundamentals of ML, showing you how to use C++ libraries to get the most out of data. C++ can make your ML models run faster and more efficiently compared to other approaches that use interpreted languages, such as Python. Also, C++ allows you to significantly reduce the negative performance impact of data conversion between different languages used in the ML model because you have direct access to core algorithms and raw data.

You will find this book useful if you want to get started with ML algorithms and techniques using the widespread C++ language. This book also appeals to data analysts, data scientists, and ML developers who are looking to implement different ML models in production using native development toolsets such as the GCC or Clang ecosystems. Working knowledge of the C++ programming language is mandatory to get started with this book.