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
- Purchase of the print or Kindle book includes a free PDF eBook
Book Description
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
Who this book is for
This book is for ML engineers, deep learning practitioners, and data scientists with a solid C++ background who want to build 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.
