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Deep Learning with C++: High-Performance Neural Networks and Model Deployment for Real-Time Applications
Not yet published
Expected 9 Sep 26Build and deploy high-performance deep learning models using C++ for real-time applications where speed and efficiency matter.
Key FeaturesImplement neural networks using the PyTorch C++ API and Caffe2Optimize and deploy deep learning models for real-time inferenceLearn CUDA acceleration, model compression, and monitoring best practicesPurchase of the print or Kindle book includes a free PDF eBookBook 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 learnSet up and use PyTorch C++ API and Caffe2 for deep learningImplement CNNs, RNNs, LSTMs, GANs, and LLMs in C++Leverage CUDA for high-performance model trainingOptimize models through quantization, pruning, and compressionDeploy and monitor models in production using C++ toolsApply explainability techniques like LIME, SHAP, and Grad-CAMWho 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.
Table of ContentsIntroduction to Deep Learning in C++ and DL Environment Setting UpData 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 InferenceDebugging and Retraining Deployed ModelsMonitoring Deployed ModelsExplainability and Transparency in Deep Learning Models
Key FeaturesImplement neural networks using the PyTorch C++ API and Caffe2Optimize and deploy deep learning models for real-time inferenceLearn CUDA acceleration, model compression, and monitoring best practicesPurchase of the print or Kindle book includes a free PDF eBookBook 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 learnSet up and use PyTorch C++ API and Caffe2 for deep learningImplement CNNs, RNNs, LSTMs, GANs, and LLMs in C++Leverage CUDA for high-performance model trainingOptimize models through quantization, pruning, and compressionDeploy and monitor models in production using C++ toolsApply explainability techniques like LIME, SHAP, and Grad-CAMWho 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.
Table of ContentsIntroduction to Deep Learning in C++ and DL Environment Setting UpData 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 InferenceDebugging and Retraining Deployed ModelsMonitoring Deployed ModelsExplainability and Transparency in Deep Learning Models
Kindle Edition
Expected publication September 9, 2026
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