Vikash Gupta – författare

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 FeaturesBuild 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 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 learnSet up and use CUDA and PyTorch's 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 forThis 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.