Chen / Gupta Deep Learning with C++

Design and deploy neural networks using CUDA for high-performance AI in C++
1. Auflage 2026
ISBN: 978-1-83588-003-6
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 0 - No protection

E-Book, Englisch, 610 Seiten

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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Autoren/Hrsg.

Chen, Bill

Gupta, Vikash

Fachgebiete

• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Software Engineering

Leseproben

Preface

Deep learning has become one of the most important technologies in modern software systems, powering applications in computer vision, speech, language, recommendation, healthcare, finance, robotics, and real-time decision-making. While most deep learning workflows are introduced in Python, many production environments ultimately demand the speed, control, and systems-level efficiency that C++ provides. This book shows you how to bridge that gap. It is written for machine learning engineers, deep learning practitioners, software engineers, and technically inclined data scientists who want to understand how deep learning systems are built, trained, optimized, deployed, and monitored in C++. Across this book, you will move from setting up a C++ deep learning environment and preparing data, to building core neural architectures such as multilayer perceptrons, convolutional networks, recurrent networks, generative models, and transformer-based systems, and finally to deploying, debugging, monitoring, and explaining production models in performance-critical environments.

C++ remains one of the strongest languages for high-performance deep learning applications because it offers tight control over memory, concurrency, CPU and GPU resources, and runtime behavior. That matters when you need low-latency inference, efficient model serving, hardware-aware optimization, or deployment on constrained systems. Yet for many practitioners, moving from Python-based experimentation to C++ implementation can feel like crossing a gap with too few practical guides. This book is designed to close that gap by combining deep learning concepts with hands-on implementation patterns in modern C++.

We begin by establishing the foundations you need to work productively. You will first review key deep learning concepts and set up a usable C++ environment for model development. From there, you will learn how to prepare and preprocess data efficiently and how to use CUDA to accelerate training and inference on GPUs. These early chapters are meant to give you both the conceptual footing and the tooling needed for serious deep learning work in C++.

Once the environment and foundations are in place, the book turns to model building and training. You will start with a basic neural network to understand forward passes, backpropagation, optimization, and evaluation in C++. You will then extend those ideas to multilayer perceptrons, convolutional neural networks for image tasks, and recurrent architectures such as Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTMs) Networks in C++ for sequential data. The book then broadens into generative modeling, covering autoencoders, Generative Adversarial Networks (GANs), and core large language model concepts, before moving into distributed training, multi-GPU execution, parallelism, and model compression strategies such as quantization and pruning.

The final section of the book focuses on what happens after a model works in development. You will learn how to prepare models for inference with formats such as TorchScript and Open Neural Network Exchange (ONNX), deploy them into cloud, on-premise, or edge settings, and optimize them for real-world performance. From there, the book covers debugging production failures, retraining models as data changes, monitoring live systems, and improving transparency with explainability techniques such as Local Interpretable Model-agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), and Gradient-weighted Class Activation Mapping (Grad-CAM). By the end, you will have a practical view of the full deep learning lifecycle in C++, from implementation to operational reliability.

This book is intentionally practical. Rather than treating C++ as just an alternative syntax for machine learning, it treats C++ as a serious engineering environment for building deployable deep learning systems. Along the way, you will see how performance concerns, software design choices, deployment constraints, and observability considerations shape the way production AI systems are built. My goal is not only to help you understand how deep learning works in C++, but also to help you write systems that are fast, robust, and suitable for real use.

In writing this book, I wanted to bring together the two worlds that many practitioners experience separately: the world of deep learning theory and experimentation, and the world of production-grade systems engineering. C++ sits at that intersection. It allows us to move beyond prototypes and into reliable, efficient implementations that can serve demanding applications at scale. My hope is that this book helps you become confident in that transition and gives you a practical foundation for building modern deep learning systems in C++.

Who this book is for

This book is for machine learning engineers, deep learning engineers, software engineers, and data scientists who want to build high-performance deep learning systems in C++. It is especially useful for readers who are already familiar with the basics of machine learning and neural networks and now want to move closer to production-grade implementation, optimization, and deployment.

You should be comfortable with core C++ programming concepts such as classes, memory management, and basic concurrency. A basic to intermediate understanding of machine learning and deep learning concepts, including neural networks, training loops, and common model architectures, will help you get the most out of this book. Familiarity with frameworks such as PyTorch or TensorFlow is useful, but prior experience with their Python APIs is enough; this book will help you translate that experience into C++ workflows.

If you are a Python-first practitioner looking to understand how deep learning systems are implemented in a lower-level, performance-sensitive setting, this book will help you make that transition. If you are a C++ developer looking to apply your systems background to modern AI workloads, this book will give you a structured path into deep learning. And if you work on real-time or resource-constrained applications where latency, throughput, and efficiency matter, this book is written with those constraints in mind.

What this book covers

, , introduces the core concepts of deep learning, explains why C++ is valuable for performance-intensive AI workloads, and walks through the setup of a practical C++ deep learning environment.

, , covers the data pipeline side of deep learning, including normalization, augmentation, efficient loading, memory-aware handling of large datasets, and custom dataset loaders in C++.

, , introduces CUDA and shows how to use GPU acceleration to speed up training and inference, optimize performance-critical code paths, and reason about bottlenecks.

, , walks through the implementation of a simple neural network, including neurons, activation functions, forward propagation, loss computation, backpropagation, and gradient-based optimization.

, , expands into deeper feedforward networks, showing how to add hidden layers, use dropout and batch normalization, tune hyperparameters, and evaluate performance on practical tasks.

, , introduces image-based data, showing how neural networks can be used to classify images, perform segmentation on images, along with various image preprocessing and augmentation techniques.

, , talks about sequential data particularly text data, covering their mathematical foundations, training algorithms (BPTT), practical C++ implementations, text processing techniques, word embeddings, and real-world applications in text prediction and neural machine translation.

, , covers foundational generative AI architectures and GANs along with autoregressive text generation principles, sampling strategies, and comprehensive evaluation metrics for assessing generative model performance.

, , explores the Transformer architecture and its components, its encoder-decoder implementations leading to distributed training strategies and model compression.

, , explains how to export models for deployment, serve them in production environments, optimize inference performance, and build real-time inference APIs in C++.

, , shows how to diagnose performance degradation, detect, and respond to model drift, improve logging, and debugging workflows, and build practical retraining strategies.

, , focuses on production observability, including tracking...