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

Chen, Bill/ Gupta, Vikash
67.99 SGD
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61.20
English

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Product Description

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

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.

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