Deep learning systems often struggle to meet performance demands in real-time and production environments. This book shows you how to build high-performance deep learning systems in C++, enabling efficient and scalable artificial intelligence (AI) in resource-constrained environments where performance matters.


You’ll start by setting up a complete C++ deep learning environment and implementing core neural networks from scratch. As you progress, you’ll build advanced architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory Networks (LSTMs), Generative Adversarial Networks (GANs), and Transformers, using C++, CUDA, and PyTorch’s C++ API. The book then focuses on model quantization and compression. It will guide you through the model deployment process in production with robust monitoring and explainability. You’ll also explore distributed training and techniques for real-time inference in performance-critical domains.


By the end of this book, you’ll be able to design, optimize, and deploy deep learning systems in C++ that are production-ready, scalable, and efficient across multiple industries.



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