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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 + ...
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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&aposs next-gen Reader*Key Features:Build deep learning models in C++ with PyTorch C++ API and CUDAImplement CNNs, RNNs, LSTMs, GANs, and Transformers in C++ for real-world applicationsOptimize and deploy machine learning models to production with scalable C++ pipelinesBook 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&aposll start by setting up a complete C++ deep learning environment and implementing core neural networks from scratch. As you progress, you&aposll 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&aposs 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&aposll also explore distributed training and techniques for real-time inference in performance-critical domains.By the end of this book, you&aposll 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&aposs C++ API for deep learningImplement CNNs, RNNs, LSTMs, GANs, Transformers, and LLMs in C++Leverage CUDA for high-performance model trainingPerform model compression using quantization, pruning, and distillationDeploy and monitor models in production using C++ toolsApply explainability techniques such as LIME, SHAP, and Grad-CAMWho 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 ContentsIntroduction to Deep Learning with C++ and Environment SetupData 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 InferenceDebugging and Retraining Deployed ModelsMonitoring Deployed ModelsExplainability and Transparency in Deep Learning Models
