Deep Learning with C++: High-Performance Neural Networks and Model Deployment for Real-Time Applications

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Build and deploy high-performance deep learning models using C++ for real-time applications where speed and efficiency matter.

Key Features

Implement neural networks using the PyTorch C++ API and Caffe2
Optimize and deploy deep learning models for real-time inference
Learn CUDA acceleration, model compression, and monitoring best practices
Deep Learning with C++ is a hands-on guide to building, optimizing, and deploying deep learning models using the power of C++. Designed for ML engineers, data scientists, and developers working in performance-critical domains, this book provides step-by-step instruction for implementing everything from basic neural networks to CNNs, RNNs, GANs, and LLMs using the PyTorch C++ API, Caffe2, and CUDA.

You will begin by setting up a C++ deep learning environment and understanding foundational neural network concepts. Then, you’ll move on to building various deep learning architectures, optimizing them for speed, and deploying them with robust monitoring and explainability features. Whether you work in finance, gaming, healthcare, or embedded systems, this book equips you to deploy deep learning systems at scale.

Complete with real-world case studies and advanced topics like distributed training, model compression, and explainability, this book ensures you’re ready for production-ready AI systems that are fast, scalable, and efficient.

What you will learn

Set up and use PyTorch C++ API and Caffe2 for deep learning
Implement CNNs, RNNs, LSTMs, GANs, and LLMs in C++
Leverage CUDA for high-performance model training
Optimize models through quantization, pruning, and compression
Deploy and monitor models in production using C++ tools
Apply explainability techniques like LIME, SHAP, and Grad-CAM