{"product_id":"hands-on-machine-learning-with-c-build-train-and-deploy-end-to-end-machine-learning-and-deep-learning-pipelines-paperback","title":"Hands-On Machine Learning with C++: Build, train, and deploy end-to-end machine learning and deep learning pipelines - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eKirill Kolodiazhnyi\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eImplement supervised and unsupervised machine learning algorithms using C++ libraries such as PyTorch C++ API, Caffe2, Shogun, Shark-ML, mlpack, and dlib with the help of real-world examples and datasets\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eKey Features\u003c\/strong\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003eBecome familiar with data processing, performance measuring, and model selection using various C++ libraries\u003c\/li\u003e \u003cli\u003eImplement practical machine learning and deep learning techniques to build smart models\u003c\/li\u003e \u003cli\u003eDeploy machine learning models to work on mobile and embedded devices\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003cstrong\u003eBook Description\u003c\/strong\u003e\u003c\/p\u003e \u003cp\u003eC++ can make your machine learning models run faster and more efficiently. This handy guide will help you learn the fundamentals of machine learning (ML), showing you how to use C++ libraries to get the most out of your data. This book makes machine learning with C++ for beginners easy with its example-based approach, demonstrating how to implement supervised and unsupervised ML algorithms through real-world examples.\u003c\/p\u003e \u003cp\u003eThis book will get you hands-on with tuning and optimizing a model for different use cases, assisting you with model selection and the measurement of performance. You'll cover techniques such as product recommendations, ensemble learning, and anomaly detection using modern C++ libraries such as PyTorch C++ API, Caffe2, Shogun, Shark-ML, mlpack, and dlib. Next, you'll explore neural networks and deep learning using examples such as image classification and sentiment analysis, which will help you solve various problems. Later, you'll learn how to handle production and deployment challenges on mobile and cloud platforms, before discovering how to export and import models using the ONNX format.\u003c\/p\u003e \u003cp\u003eBy the end of this C++ book, you will have real-world machine learning and C++ knowledge, as well as the skills to use C++ to build powerful ML systems.\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eWhat you will learn\u003c\/strong\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003eExplore how to load and preprocess various data types to suitable C++ data structures\u003c\/li\u003e \u003cli\u003eEmploy key machine learning algorithms with various C++ libraries\u003c\/li\u003e \u003cli\u003eUnderstand the grid-search approach to find the best parameters for a machine learning model\u003c\/li\u003e \u003cli\u003eImplement an algorithm for filtering anomalies in user data using Gaussian distribution\u003c\/li\u003e \u003cli\u003eImprove collaborative filtering to deal with dynamic user preferences\u003c\/li\u003e \u003cli\u003eUse C++ libraries and APIs to manage model structures and parameters\u003c\/li\u003e \u003cli\u003eImplement a C++ program to solve image classification tasks with LeNet architecture\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003cstrong\u003eWho this book is for\u003c\/strong\u003e\u003c\/p\u003e \u003cp\u003eYou will find this C++ machine learning book useful if you want to get started with machine learning algorithms and techniques using the popular C++ language. As well as being a useful first course in machine learning with C++, this book will also appeal to data analysts, data scientists, and machine learning developers who are looking to implement different machine learning models in production using varied datasets and examples. Working knowledge of the C++ programming language is mandatory to get started with this book.\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 530\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 1.07 x 9.25 x 7.5 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e May 15, 2020\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":52493478560051,"sku":"9781789955330","price":97.7,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0300\/5595\/6612\/files\/NlIwVmtWWkkvSmtkejdiMnFFa3d3Zz09.webp?v=1759953613","url":"https:\/\/www.vysn.com\/en-ca\/products\/hands-on-machine-learning-with-c-build-train-and-deploy-end-to-end-machine-learning-and-deep-learning-pipelines-paperback","provider":"VYSN","version":"1.0","type":"link"}