Hands-On Machine Learning with C++ - Second Edition

<p><strong>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 datasets</strong></p><p><strong>Key Features:</strong></p><p>- Familiarize yourself with data processing performance measuring and model selection using various C++ libraries</p><p>- Implement practical machine learning and deep learning techniques to build smart models</p><p>- Deploy machine learning models to work on mobile and embedded devices</p><p>- Purchase of the print or Kindle book includes a free PDF eBook</p><p><strong>Book Description:</strong></p><p>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.</p><p>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.</p><p>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++.</p><p>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.</p><p><strong>What You Will Learn:</strong></p><p>- Employ key machine learning algorithms using various C++ libraries</p><p>- Load and pre-process different data types to suitable C++ data structures</p><p>- Find out how to identify the best parameters for a machine learning model</p><p>- Use anomaly detection for filtering user data</p><p>- Apply collaborative filtering to manage dynamic user preferences</p><p>- Utilize C++ libraries and APIs to manage model structures and parameters</p><p>- Implement C++ code for object detection using a modern neural network</p><p><strong>Who this book is for:</strong></p><p>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.</p><p><strong>Table of Contents</strong></p><p>- Introduction to Machine Learning with C++</p><p>- Data Processing</p><p>- Measuring Performance and Selecting Models</p><p>- Clustering</p><p>- Anomaly Detection</p><p>- Dimensionality Reduction</p><p>- Classification</p><p>- Recommender Systems</p><p>- Ensemble Learning</p><p>- Neural Networks for Image Classification</p><p>- Sentiment Analysis with BERT and Transfer Learning</p><p>- Exporting and Importing Models</p><p>- Tracking and Visualizing ML Experiments</p><p>- Deploying Models on a Mobile Platform</p>