Artículo: AMZ-B0G1N21D5W

Mastering Deep Learning With Pytorch 2.X: Practical Projects In computer Vision, NLP, and Generative AI using Python and Pytorch lightning

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  • Title: Mastering Deep Learning with PyTorch 2.x Subtitle: Practical Projects in Computer Vision, NLP, and Generative AI Using Python and PyTorch Lightning Book Description: Build, train, and deploy state-of-the-art AI models with the power of PyTorch 2.x—the next-generation deep learning framework trusted by researchers, engineers, and innovators worldwide. Mastering Deep Learning with PyTorch 2.x is your complete hands-on guide to designing intelligent systems that see, read, and create. From computer vision and natural language processing to generative AI and model optimization, this book takes you step-by-step through practical projects that bridge theory and real-world implementation. Whether you’re a student, developer, or AI professional, you’ll learn how to go beyond basic models and build robust, production-ready deep learning solutions using Python, PyTorch Lightning, and modern MLOps tools. Inside this book, you will learn how to: • Master PyTorch 2.x fundamentals, tensors, autograd, and neural network modules • Build powerful CNNs, Transformers, and Diffusion Models for real-world applications • Implement transfer learning with ResNet, EfficientNet, and Vision Transformers • Train LLMs and NLP models for text classification, summarization, and sentiment analysis • Create Generative AI systems using VAEs, GANs, and Stable Diffusion fine-tuning • Optimize performance with mixed precision, quantization, pruning, and distributed training • Integrate experiment tracking, explainability tools (Captum, Grad-CAM), and fairness analysis • Deploy deep learning models using FastAPI, TorchServe, Docker, and ONNX You’ll build hands-on projects such as: • Medical image tumor detection with CNNs • Transformer-based text summarization and sentiment analysis • Custom image generation using Stable Diffusion • Real-time AI-powered recommendation and retrieval systems Why this book? Unlike theory-heavy AI textbooks, this guide is built around actionable projects, clear explanations, and industry-ready workflows. Every chapter moves you closer to mastering how modern deep learning systems are developed, optimized, and deployed at scale. Who this book is for: • AI and ML practitioners seeking practical, end-to-end examples • Python developers eager to specialize in deep learning • Data scientists transitioning from experimentation to deployment • Students and professionals looking to master PyTorch 2.x for career growth By the end of this book, you will: • Build and train advanced AI models with PyTorch 2.x • Apply deep learning to vision, text, and generative tasks • Deploy scalable, interpretable, and ethical AI systems with confidence
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