Deep Learning with C++: Design and deploy neural networks using CUDA for high-performance AI in C++
暫譯: 使用 C++ 的深度學習：利用 CUDA 設計和部署高效能 AI 的神經網絡

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

Introduction to Deep Learning with C++ and Environment Setup

Data Preparation and Preprocessing in C++

CUDA for GPU Acceleration in Deep Learning with C++

Building a Basic Neural Network in C++

Multilayer Perceptrons in C++

Convolutional Neural Networks in C++

Recurrent Neural Networks and Long Short-Term Memory Networks in C++

Generative Networks, Autoencoders, and Large Language Models in C++

Transformers and Large Language Model Fine-tuning in C++

Deploying and Optimizing Models for Inference

Debugging and Retraining Deployed Models

Monitoring Deployed Models

Explainability and Transparency in Deep Learning Models