Deep Learning with C++

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.

Table of Contents

1. Introduction to Deep Learning with C++ and Environment Setup
2. Data Preparation and Preprocessing in C++
3. CUDA for GPU Acceleration in Deep Learning with C++
4. Building a Basic Neural Network in C++
5. Multilayer Perceptron's in C++
6. Convolutional Neural Networks in C++
7. Recurrent Neural Networks and Long Short-Term Memory Networks in C++
8. Generative Networks, Autoencoders, and Large Language Models in C++
9. Transformers and Large Language Model Fine-tuning in C++
10. Deploying and Optimizing Models for Inference
11. Debugging and Retraining Deployed Models
12. Monitoring Deployed Models
13. Explainability and Transparency in Deep Learning Models

Bill Chen is a machine learning engineer at Meta specializing in deep learning, CUDA, and C++. He holds a PhD in Bioinformatics from the University of Kentucky and has worked in both production and instructional roles in applied AI. He has taught at the NVIDIA Deep Learning Institute, earned the NVIDIA-Certified Associate: Generative AI Multimodal credential, and served as part-time machine learning faculty at UCSC Silicon Valley Extension. His work includes Facebook group search modeling and surgical duration prediction. In this book, he combines industry experience and teaching to guide readers in building high-performance deep learning systems in C++.

Vikash Gupta Ph.D., is a Senior Solutions Architect at Amazon Web Services (AWS), based in Seattle, Washington. He earned his Ph.D. in Computational Biology from INRIA, France, where his research centered on neuroimaging and statistical modeling. At AWS, he applies deep learning and artificial intelligence to advance medical imaging technologies, contributing to open-source initiatives such as the MONAI framework for healthcare. He also served as a research scientist at The Ohio State University and as an Assistant Professor at Mayo Clinic. He has authored more than 60 peer-reviewed publications.