Da: Books From California, Simi Valley, CA, U.S.A.
EUR 32,61
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Aggiungi al carrellopaperback. Condizione: Good. Mismanufactored, with part of the cover bigger than the rest of the pages.
Da: California Books, Miami, FL, U.S.A.
EUR 49,79
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Editore: Packt Publishing 1/24/2025, 2025
ISBN 10: 1805120573 ISBN 13: 9781805120575
Lingua: Inglese
Da: BargainBookStores, Grand Rapids, MI, U.S.A.
EUR 49,18
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Aggiungi al carrelloPaperback or Softback. Condizione: New. Hands-On Machine Learning with C++ - Second Edition: Build, train, and deploy end-to-end machine learning and deep learning pipelines 1.92. Book.
Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 53,22
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Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 46,78
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Da: California Books, Miami, FL, U.S.A.
EUR 59,75
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Editore: Packt Publishing 5/15/2020, 2020
ISBN 10: 1789955335 ISBN 13: 9781789955330
Lingua: Inglese
Da: BargainBookStores, Grand Rapids, MI, U.S.A.
EUR 58,29
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Aggiungi al carrelloPaperback or Softback. Condizione: New. Hands-On Machine Learning with C++: Build, train, and deploy end-to-end machine learning and deep learning pipelines 1.98. Book.
Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 52,98
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Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 53,21
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Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 62,11
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 57,98
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Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Editore: Packt Publishing 2020-05-15, 2020
ISBN 10: 1789955335 ISBN 13: 9781789955330
Lingua: Inglese
Da: Chiron Media, Wallingford, Regno Unito
EUR 59,39
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Aggiungi al carrelloPaperback. Condizione: New.
Da: Books Puddle, New York, NY, U.S.A.
EUR 81,86
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Da: Mispah books, Redhill, SURRE, Regno Unito
EUR 72,11
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Aggiungi al carrelloPaperback. Condizione: New. New. book.
Da: Lucky's Textbooks, Dallas, TX, U.S.A.
EUR 53,02
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Da: HPB-Red, Dallas, TX, U.S.A.
EUR 33,27
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Aggiungi al carrellopaperback. Condizione: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
Da: THE SAINT BOOKSTORE, Southport, Regno Unito
EUR 58,72
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Aggiungi al carrelloPaperback / softback. Condizione: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 526.
Da: THE SAINT BOOKSTORE, Southport, Regno Unito
EUR 69,77
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Aggiungi al carrelloPaperback / softback. Condizione: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 1117.
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 73,88
Convertire valutaQuantità: 1 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Apply supervised and unsupervised machine learning algorithms using C++ libraries, such as PyTorch C++ API, Flashlight, Blaze, mlpack, and dlib using real-world examples and datasetsKey Features: Familiarize yourself with data processing, performance measuring, and model selection using various C++ libraries Implement practical machine learning and deep learning techniques to build smart models Deploy machine learning models to work on mobile and embedded devices Purchase of the print or Kindle book includes a free PDF Elektronisches BuchBook Description:Written by a seasoned software engineer with several years of industry experience, this book will teach you the basics of machine learning (ML) and show you how to use C++ libraries, along with helping you create supervised and unsupervised ML models.You'll gain hands-on experience in tuning and optimizing a model for various use cases, enabling you to efficiently select models and measure performance. The chapters cover techniques such as product recommendations, ensemble learning, anomaly detection, sentiment analysis, and object recognition using modern C++ libraries. You'll also learn how to overcome production and deployment challenges on mobile platforms, and see how the ONNX model format can help you accomplish these tasks.This new edition has been updated with key topics such as sentiment analysis implementation using transfer learning and transformer-based models, as well as tracking and visualizing ML experiments with MLflow. An additional section shows you how to use Optuna for hyperparameter selection. The section on model deployment into mobile platform now includes a detailed explanation of real-time object detection for Android with C++.By the end of this C++ book, you'll have real-world machine learning and C++ knowledge, as well as the skills to use C++ to build powerful ML systems.What You Will Learn: Employ key machine learning algorithms using various C++ libraries Load and pre-process different data types to suitable C++ data structures Find out how to identify the best parameters for a machine learning model Use anomaly detection for filtering user data Apply collaborative filtering to manage dynamic user preferences Utilize C++ libraries and APIs to manage model structures and parameters Implement C++ code for object detection using a modern neural networkWho this book is for:This book is for beginners looking to explore machine learning algorithms and techniques using C++. This book is also valuable for data analysts, scientists, and developers who want to implement machine learning models in production. Working knowledge of C++ is needed to make the most of this book.Table of Contents Introduction to Machine Learning with C++ Data Processing Measuring Performance and Selecting Models Clustering Anomaly Detection Dimensionality Reduction Classification Recommender Systems Ensemble Learning Neural Networks for Image Classification Sentiment Analysis with BERT and Transfer Learning Exporting and Importing Models Tracking and Visualizing ML Experiments Deploying Models on a Mobile Platform.
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 76,91
Convertire valutaQuantità: 1 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.
Da: Majestic Books, Hounslow, Regno Unito
EUR 83,56
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Aggiungi al carrelloCondizione: New. Print on Demand.
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 86,73
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Aggiungi al carrelloCondizione: New. PRINT ON DEMAND.