Deep Learning with C++
Packt Publishing Limited (Verlag)
978-1-83588-002-9 (ISBN)
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 DescriptionDeep 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 forThis 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.
Xi Chen has graduated with Ph.D. in Biochemical and a Master in Statistics from the University of Kentucky. He is working as a certified NVidia Computer Vision (CV), CUDA and Deep Learning instructor. During his graduate career, he has led CV and deep learning related workshops. He also has published papers on topics of autonomic driving, reinforcement learning, and deep learning. Vikash Gupta, Ph.D., CIIP, is a Senior Research Scientist 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. A Certified Imaging Informatics Professional, he has authored over 15 peer-reviewed publications
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
| Erscheinungsdatum | 30.10.2025 |
|---|---|
| Verlagsort | Birmingham |
| Sprache | englisch |
| Maße | 191 x 235 mm |
| Themenwelt | Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| ISBN-10 | 1-83588-002-9 / 1835880029 |
| ISBN-13 | 978-1-83588-002-9 / 9781835880029 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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