{"product_id":"deep-learning-with-c-design-and-deploy-neural-networks-using-cuda-for-high-performance-ai-in-c-paperback","title":"Deep Learning with C++: Design and deploy neural networks using CUDA for high-performance AI in C++ - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eBill Chen\u003c\/b\u003e (Author), \u003cb\u003eVikash Gupta\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eBuild and deploy high-performance deep learning models using C++ for real-time applications where speed and efficiency matter.\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eFree with your book: DRM-free PDF version + access to Packt's next-gen Reader*\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eKey Features: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eBuild deep learning models in C++ with PyTorch C++ API and CUDA\u003c\/p\u003e\u003cp\u003eImplement CNNs, RNNs, LSTMs, GANs, and Transformers in C++ for real-world applications\u003c\/p\u003e\u003cp\u003eOptimize and deploy machine learning models to production with scalable C++ pipelines\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eBook Description: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eDeep 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.\u003c\/p\u003e\u003cp\u003eYou'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.\u003c\/p\u003e\u003cp\u003eBy 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.\u003c\/p\u003e\u003cp\u003e*Email sign-up and proof of purchase required\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWhat You Will Learn: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eSet up and use CUDA and PyTorch's C++ API for deep learning\u003c\/p\u003e\u003cp\u003eImplement CNNs, RNNs, LSTMs, GANs, Transformers, and LLMs in C++\u003c\/p\u003e\u003cp\u003eLeverage CUDA for high-performance model training\u003c\/p\u003e\u003cp\u003ePerform model compression using quantization, pruning, and distillation\u003c\/p\u003e\u003cp\u003eDeploy and monitor models in production using C++ tools\u003c\/p\u003e\u003cp\u003eApply explainability techniques such as LIME, SHAP, and Grad-CAM\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWho this book is for: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eThis 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.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eTable of Contents\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eIntroduction to Deep Learning with C++ and Environment Setup\u003c\/p\u003e\u003cp\u003eData Preparation and Preprocessing in C++\u003c\/p\u003e\u003cp\u003eCUDA for GPU Acceleration in Deep Learning with C++\u003c\/p\u003e\u003cp\u003eBuilding a Basic Neural Network in C++\u003c\/p\u003e\u003cp\u003eMultilayer Perceptrons in C++\u003c\/p\u003e\u003cp\u003eConvolutional Neural Networks in C++\u003c\/p\u003e\u003cp\u003eRecurrent Neural Networks and Long Short-Term Memory Networks in C++\u003c\/p\u003e\u003cp\u003eGenerative Networks, Autoencoders, and Large Language Models in C++\u003c\/p\u003e\u003cp\u003eTransformers and Large Language Model Fine-tuning in C++\u003c\/p\u003e\u003cp\u003eDeploying and Optimizing Models for Inference\u003c\/p\u003e\u003cp\u003eDebugging and Retraining Deployed Models\u003c\/p\u003e\u003cp\u003eMonitoring Deployed Models\u003c\/p\u003e\u003cp\u003eExplainability and Transparency in Deep Learning Models\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 610\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 1.23 x 9.25 x 7.5 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e April 30, 2026\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":53425652039987,"sku":"9781835880029","price":78.98,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0300\/5595\/6612\/files\/8Z41oDsGWk9781835880029.webp?v=1780520073","url":"https:\/\/www.vysn.com\/products\/deep-learning-with-c-design-and-deploy-neural-networks-using-cuda-for-high-performance-ai-in-c-paperback","provider":"VYSN","version":"1.0","type":"link"}