Deep Learning with C++: Design and deploy neural networks using CUDA for high-performance AI in C++

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Bill Chen Vikash Gupta

Deep Learning with C++

Name: Deep Learning with C++
Price: 90.51 AUD
Availability: OutOfStock
ISBN: 9781835880029

Design and deploy neural networks using CUDA for high-performance AI in C++

Bill Chen, Vikash Gupta

$112.95 $90.51

Paperback

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English

Packt Publishing Limited
30 April 2026

Programming & scripting languages: general; Neural networks & fuzzy systems

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

Free with your book: DRM-free PDF version + access to Packt's next-gen Reader
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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 DescriptionDeep 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.
*Email sign-up and proof of purchase requiredWhat 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 forThis 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.

By: Bill Chen, Vikash Gupta
Imprint: Packt Publishing Limited
Country of Publication: United Kingdom
Dimensions: Height: 235mm, Width: 191mm,
ISBN: 9781835880029
ISBN 10: 1835880029
Pages: 610
Publication Date: 30 April 2026
Audience: Professional and scholarly , Undergraduate
Format: Paperback
Publisher's Status: Active

Table of Contents Introduction to Deep Learning in C++ and DL Environment Setting Up Data Preparation and Preprocessing in C++ CUDA for GPU Acceleration in Deep Learning with C++ Building a Basic Neural Network in C++ Multilayer Perceptrons (MLPs) in C++ Convolutional Neural Networks (CNNs) in C++ Recurrent Neural Networks (RNNs) and LSTMs in C++ Generative Networks, Autoencoders, and LLM in C++ Distributed Training, Parallelism, and Model Compression in C++ Deploying and Optimizing Models for Inference Debugging and Retraining Deployed Models Monitoring Deployed Models 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.