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Hands-On Machine Learning with C++: Build, train, and deploy end-to-end machine learning and deep learning pipelines
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Hands-On Machine Learning with C++: Build, train, and deploy end-to-end machine learning and deep learning pipelines
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.
Hands-On Machine Learning with C++: Build, train, and deploy end-to-end machine learning and deep learning pipelines
Item #: 32273991

Hands-On Machine Learning with C++: Build, train, and deploy end-to-end machine learning and deep learning pipelines

Item #: 32273991

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    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.
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    What Stands Out

    End-to-End Pipeline
    Offers comprehensive guidance on building, training, and deploying machine learning models, ensuring users can implement complete solutions from start to finish.
    C++ Focus
    Utilizes C++ for performance-driven applications, making it ideal for software engineers looking to integrate machine learning into high-performance systems.
    Practical Learning
    Emphasizes hands-on projects to enhance understanding, allowing readers to apply concepts in real-world scenarios effectively.

    Product Details

    Discover the power of machine learning and deep learning with C. Build, train, and deploy end-to-end ML pipelines. Shop now at Ubuy Saudi Arabia.
    • Implement 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 datasetsKey FeaturesBecome familiar with data processing, performance measuring, and model selection using various C++ librariesImplement practical machine learning and deep learning techniques to build smart modelsDeploy machine learning models to work on mobile and embedded devicesBook DescriptionC++ 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.This 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.By 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.What you will learnExplore how to load and preprocess various data types to suitable C++ data structuresEmploy key machine learning algorithms with various C++ librariesUnderstand the grid-search approach to find the best parameters for a machine learning modelImplement an algorithm for filtering anomalies in user data using Gaussian distributionImprove collaborative filtering to deal with dynamic user preferencesUse C++ libraries and APIs to manage model structures and parametersImplement a C++ program to solve image classification tasks with LeNet architectureWho this book is forYou 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.Table of ContentsIntroduction to Machine Learning with C++Data ProcessingMeasuring Performance and Selecting ModelsClusteringAnomaly DetectionDimensionality ReductionClassificationRecommender SystemsEnsemble LearningNeural Networks for Image ClassificationSentiment Analysis with Recurrent Neural NetworksExporting and Importing ModelsDeploying Models on Mobile and Cloud Platforms
    Publisher Packt Publishing
    Publication date May 15, 2020
    Language English
    Print length 530 pages
    ISBN-10 1789955335
    ISBN-13 978-1789955330
    Item Weight 2.21 pounds (1 kg)
    Dimensions 7.5 x 1.2 x 9.25 inches (19.1 x 3 x 23.5 cm)

    Who Should Buy?

    Suitable For
    • Beginner Developers

      New developers can learn machine learning concepts effectively through practical examples and hands-on projects with C++.

    • Data Scientists

      Data scientists looking to implement machine learning models in production will benefit from end-to-end project coverage.

    • C++ Enthusiasts

      C++ programmers eager to apply their skills to machine learning will find this book particularly useful and engaging.

    Not Suitable For
    • Non-Technical Users

      Users without a programming background may struggle with the technical content and programming requirements of the book.

    • Python Users

      Developers accustomed to Python may find C++ implementations unfamiliar and thus may not find the content applicable.

    • Casual Learners

      Those seeking a light introduction to machine learning without deep coding might find this book overly complex.

    Product Description

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    Customer Questions & Answers

    • Question: What is 'Hands-On Machine Learning with C' about?

      Answer: This book offers a comprehensive introduction to building, training, and deploying machine learning and deep learning models using C. It guides readers through practical applications and hands-on projects, which include creating data pipelines, preprocessing data, and implementing algorithms. The structured approach makes it ideal for both beginners and seasoned developers looking to enhance their understanding of machine learning concepts through practical coding examples.
    • Question: Who is the target audience for this book?

      Answer: The primary audience for 'Hands-On Machine Learning with C' includes software developers, data scientists, and students interested in machine learning. It’s perfect for those with a background in programming seeking to delve into ML concepts. The book assumes a fundamental understanding of C programming and queuing theory, catering to readers who are looking to engage in project-based learning while strengthening their skills in machine learning applications.
    • Question: What types of projects can I expect to find in this book?

      Answer: The book contains a variety of practical projects, including building a simple machine learning model, implementing neural networks from scratch, and deploying models. These projects illustrate key concepts while enabling readers to apply what they learn in a hands-on manner. For example, you may work on tasks such as image classification or predictive analytics, which are highly relevant in real-world applications across different industries.
    • Question: Does this book cover deep learning as well?

      Answer: Yes, 'Hands-On Machine Learning with C' thoroughly covers both machine learning and deep learning. It dives into essential topics such as neural networks, convolutional neural networks, and sequential models. By integrating deep learning with practical examples, readers can better grasp how to leverage these advanced techniques for more complex datasets and applications, enhancing their ability to work on cutting-edge projects in machine learning.
    • Question: Is prior knowledge of machine learning required?

      Answer: While prior knowledge of machine learning isn’t strictly necessary, some familiarity with basic concepts can be beneficial. The book starts with fundamental principles but quickly progresses to more complex topics. If you're new to the field, you might find it useful to complement your learning with introductory resources. Engaging with this book will strengthen your coding skills while building your foundational understanding of machine learning methods.
    • Question: Can I apply the techniques learned in this book to real-world scenarios?

      Answer: Absolutely! The techniques detailed in 'Hands-On Machine Learning with C' are designed for real-world applications. Each project not only helps you understand the theoretical aspects but also prepares you for industry practices. You can apply your skills in various fields, such as finance for algorithmic trading, healthcare for predictive analytics, or even in developing smart applications that rely on machine learning capabilities.
    • Question: What programming aspects are covered in the book?

      Answer: The book covers several programming aspects relevant to machine learning with C, including data handling, model training, and performance optimization techniques. It teaches you how to manipulate data using C libraries and implement machine learning algorithms from scratch, giving you a robust understanding of the underlying mechanics. These skills are invaluable for any programmer looking to enhance their ability to work with data-intensive applications.
    • Question: How does the book facilitate learning for beginners?

      Answer: The book is structured to facilitate learning through a project-oriented approach, making it accessible for beginners. Each chapter builds on the previous one, gradually introducing more complex topics and ensuring comprehension. Additionally, the authors include clear explanations of concepts and coding practices, enabling beginners to follow along with confidence and apply what they learn directly to their projects.
    • Question: Are there any online resources or communities associated with this book?

      Answer: Yes, many readers often share insights and discuss problems related to the book in various online communities, such as GitHub and forums dedicated to machine learning. These platforms allow readers to collaborate, share their projects, and seek help on challenging sections. Engaging in these communities can significantly enhance your learning experience as you exchange ideas and solutions with peers who share your interest in machine learning.
    • Question: Where can I buy 'Hands-On Machine Learning with C' in Saudi Arabia?

      Answer: You can purchase 'Hands-On Machine Learning with C' from Ubuy, a reliable online retail platform that caters to various products including educational materials like this book. Ubuy often offers a straightforward shopping experience, ensuring you have access to this essential resource for enhancing your machine learning skills.

    Natural Language Processing Editorial Review

    Hands-On Machine Learning with C++ is a valuable resource for C++ programmers looking to enter the world of machine learning and deep learning. The book fills a significant gap in educational resources for C++ developers interested in these fields. The author provides clear explanations of the mathematical theory, along with complete examples that allow readers to immediately apply their knowledge to real-life projects. The book strikes a good balance between theory and implementation, making it accessible to developers new to data science. One of the standout features is the inclusion of a PyTorch Deep Learning library for C++ code use, enabling high-performance GPU programming with a tensor interface. Overall, this book is a time-saving tool that expands the programming scope of C++ developers without the need to transition to Python.

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    Pros

    • Clear explanations of mathematical theory
    • Complete examples with real-life datasets
    • Accessible for developers new to data science
    • Includes a PyTorch Deep Learning library for C++ code use
    • Saves time and expands the programming scope of C++ developers

    Cons

    • Requires a Visual Studio 2019 configuration for the examples

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    SAR 228

    SAR 312

    Order now and get it around Wednesday, June 24

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    Features & Benefits

    • Learn the fundamentals of machine learning using C++ libraries.
    • Implement supervised and unsupervised ML algorithms through real-world examples.
    • Tune and optimize models for different use cases, measure performance.
    • Handle production and deployment challenges on mobile and cloud platforms.
    • Use C++ to build powerful ML systems.
    • Suitable for beginners and professionals with working knowledge of C++.