Deep Learning with C++
Design and deploy neural networks using CUDA for high-performance AI in C++
BCCreated by Vikash Gupta, Bill Chen
Explore how to design and deploy advanced neural networks using C++ and CUDA for high-performance AI. Learn to build, optimize, and scale deep learning models that meet the demands of real-time and production environments. Gain practical skills for efficient model deployment across various industries.
Packt | Apr 2026 | 610 min
What You Will Learn
You will start by setting up a deep learning environment in C++ and gradually implement core and advanced neural network architectures. Through hands-on coding and real-world examples, you will practice optimizing, compressing, and deploying models. Each step focuses on building production-ready solutions with a focus on efficiency and scalability.
Key Features
- Build and train neural networks in C++ using CUDA and the PyTorch C++ API
- Optimize deep learning models for real-time performance and production deployment
- Apply model compression, monitoring, and explainability techniques for robust AI
Target Audience
Ideal for machine learning engineers, deep learning practitioners, and data scientists with a C++ background who want to build high-performance models. Also suited for developers moving from Python frameworks and aiming to deploy real-time AI solutions in fields like finance, autonomous systems, or healthcare.
