Practical C++ Machine Learning

Practical C++ Machine Learning introduces C++ programmers to the world of machine learning. If you know C++ but haven't worked with machine learning solutions before, this book is a good place to start learning the basics and experimenting with the language's essential concepts and techniques.

The book starts off by showing you how to set up a development environment and put together some basic neural networks using the Flashlight library. It then covers essential tasks like data preprocessing, model training, and evaluation, with practical examples that show how machine learning works in a C++ context. You will also learn strategies for dealing with common problems like overfitting and performance optimization. The next few chapters get into more complex topics like convolutional neural networks, model deployment, and some key performance tuning techniques. This will help you develop and integrate your own models into applications.

By the end of the book, you will have essential hands-on experience and a better clarity to explore and expand your machine learning knowledge in C++. This book doesn't aim to cover everything, but it does serve as a good starting point for you to confidently dive into the world of machine learning and deep learning.

Key Learnings

• Use Flashlight to set up a C++ environment for machine learning projects.
• Implement neural networks from scratch to gain a hands-on understanding.
• Preprocess and augment data effectively to improve model performance.
• Train and evaluate models using appropriate loss functions and metrics.
• Explore overfitting challenges with techniques like regularization and dropout.
• Build advanced architectures like ResNet.
• Apply transfer learning to leverage pre-trained models.
• Deploy models and integrate them into real-world C++ apps.
• Implement real-time inference with optimized performance.
• Improve performance using GPU acceleration and multi-threading techniques.

Table of Content

1. Getting Started with C++ Machine Learning
2. Data Handling and Preprocessing
3. Building a Simple Neural Network
4. Training Deep Neural Networks
5. Convolutional Neural Networks
6. Improving Model Performance
7. Advanced Neural Network Architectures
8. Deployment and Integration
9. Parallelism and Performance Scaling