Hands-On Machine Learning with C++

What this book covers

Hands-On Machine Learning with C++'s example-based approach will show you how to implement supervised and unsupervised ML algorithms with the help of real-world examples. The book also gives you hands-on experience of tuning and optimizing a model for different use cases, helping you to measure performance and model selection. You'll then cover techniques such as object classification and clusterization, product recommendations, ensemble learning, and anomaly detection using modern C++ libraries such as the PyTorch C++ API, Caffe2, Shogun, Shark-ML, mlpack, and dlib. Moving ahead, the chapters will take you through neural networks and deep learning using examples such as image classification and sentiment analysis, which ...