Build and deploy high-performance deep learning models using C++ for real-time applications where speed and efficiency matter.
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Key Features
- Build deep learning models in C++ with PyTorch C++ API and CUDA
- Implement CNNs, RNNs, LSTMs, GANs, and Transformers in C++ for real-world applications
- Optimize and deploy machine learning models to production with scalable C++ pipelines
Book Description
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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What you will learn
- Set up and use CUDA and PyTorch's C++ API for deep learning
- Implement CNNs, RNNs, LSTMs, GANs, Transformers, and LLMs in C++
- Leverage CUDA for high-performance model training
- Perform model compression using quantization, pruning, and distillation
- Deploy and monitor models in production using C++ tools
- Apply explainability techniques such as LIME, SHAP, and Grad-CAM
Who this book is for
This book is for ML engineers, deep learning practitioners, and data scientists with a C++ background who want to build or learn about high-performance deep learning models. It also serves developers transitioning from Python-based frameworks looking for real-time deployment solutions in industries like finance, autonomous systems, and healthcare.
