Deep 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.

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Spis treści

Deep Learning with C++. High-Performance Neural Networks and Model Deployment for Real-Time Applications eBook -- spis treści

• 1. Introduction to Deep Learning in C++ and DL Environment Setting Up
• 2. Data Preparation and Preprocessing in C++
• 3. CUDA for GPU Acceleration in Deep Learning with C++
• 4. Building a Basic Neural Network in C++
• 5. Multilayer Perceptrons (MLPs) in C++
• 6. Convolutional Neural Networks (CNNs) in C++
• 7. Recurrent Neural Networks (RNNs) and LSTMs in C++
• 8. Generative Networks, Autoencoders, and LLM in C++
• 9. Distributed Training, Parallelism, and Model Compression in C++
• 10. Deploying and Optimizing Models for Inference
• 11. Debugging and Retraining Deployed Models
• 12. Monitoring Deployed Models
• 13. Explainability and Transparency in Deep Learning Models