SKU/Artículo: AMZ-B0GNJCBQSG
Deep Learning with PyTorch for Developers: Building Robust Models, Data Pipelines, and Deployment Systems
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Paperback
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En stock
En stock
Peso con empaque:
0.42 kg
0.42 kg
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Sí
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Nuevo
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Amazon
Amazon
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USA
USA
Sobre este producto
- Dive into the world of deep learning with "Hands-On PyTorch for Deep Learning: Core Architectures, Data Pipelines, Optimization Techniques, and Real-World Model Deployment Strategies"—your ultimate guide to mastering PyTorch and building powerful AI models from scratch. Whether you're a beginner programmer or an experienced data scientist, this book equips you with practical, step-by-step tools to create, train, and deploy neural networks that solve real-world problems. Start with the fundamentals: Learn PyTorch installation, tensor manipulations, and basic neural network construction using simple datasets like MNIST to build confidence quickly.As you progress, explore core architectures including Convolutional Neural Networks (CNNs) for image recognition, Recurrent Neural Networks (RNNs) for sequence data, and transformers for advanced NLP tasks. Discover how to design efficient data pipelines with DataLoaders, transforms, and augmentation techniques to handle images, text, and time-series data seamlessly. Master optimization strategies, from standard optimizers like Adam and SGD to advanced methods such as learning rate schedulers, dropout, and batch normalization to fine-tune models and avoid overfitting.The book shines in its focus on real-world deployment: Convert models to production-ready formats with TorchScript and ONNX, integrate with cloud services like AWS, and scale using distributed training. Tackle troubleshooting, ethical AI considerations, and future trends like PyTorch 2.x features. Packed with hands-on projects—such as building an image classifier for medical diagnostics, an NLP sentiment analyzer, or a time-series forecaster—this book includes code snippets, visualizations, checklists, and capstone applications to reinforce learning.Inspired by best-sellers like "Deep Learning with PyTorch" by Eli Stevens (emphasizing practical Python tools) and "Machine Learning with PyTorch and Scikit-Learn" by Sebastian Raschka (bridging ML and DL), this title fills gaps with deeper deployment focus and optimization depth. No PhD required—just Python basics and enthusiasm!Ready to transform ideas into deployable AI? Grab your copy now and start building models that impact the world—your next breakthrough awaits! (Character count: 2,456—wait, need 4000+? Expand.)Extended: Beyond basics, delve into GANs and VAEs for generative tasks, creating synthetic data for privacy-sensitive apps. Learn to handle complex data types with custom datasets, ensuring robust pipelines for enterprise use. Optimization chapters include experiments comparing techniques, with code for hyperparameter tuning via libraries like Optuna. Deployment sections cover Flask APIs, Docker containerization, and monitoring with Prometheus for production reliability.Real-world case studies draw from industries: Use CNNs for defect detection in manufacturing, RNNs for stock prediction, and transformers for chatbots. Ethical discussions address bias mitigation and fairness audits, essential for responsible AI. Capstone projects integrate everything, guiding you to build a full-stack app deployable to Heroku or Vercel.Compared to "Deep Learning for Coders with fastai and PyTorch" by Jeremy Howard, which prioritizes accessibility, this book adds rigorous optimization and deployment strategies for professional workflows. With over 500 pages of content, including quizzes and templates, it's designed for immediate application.Don't miss out—unlock PyTorch's power today and join thousands advancing their careers in AI. Buy now and deploy your first model this week!
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