Deep Learning with PyTorch 2.x: Beginner's Guide to Neural Networks, Computer Vision, and Model Training with Python (Modern Deep Learning with PyTorch)
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Paperback
En stock
0.38 kg
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Nuevo
Amazon
USA
- Unlock the Power of PyTorch 2.x to Build Real Deep Learning Projects—Even If You're Just Starting OutAre you eager to dive into deep learning but unsure where to begin? Deep Learning with PyTorch 2.x is your practical, no-fluff guide to building real neural networks, training image classifiers, and deploying production-ready models using Python and the latest PyTorch 2.x features. Whether you're a student, data science enthusiast, software developer, or aspiring AI engineer, this book walks you step by step from the basics of tensors to powerful applications like object detection, image segmentation, and sentiment analysis.Unlike generic tutorials or outdated code snippets, this hands-on guide delivers concise, complete, and fully working examples using up-to-date libraries and real-world projects. You'll learn how to implement convolutional neural networks (CNNs), recurrent networks (RNNs), transformers, and performance optimizations using AMP, TorchDynamo, and TorchServe. By the end, you’ll be confident deploying deep learning models across desktop, cloud, mobile, and edge platforms.Written by Caesar Daniel, a trusted name in accessible AI learning resources, this guide reflects current trends in machine learning and empowers readers to move beyond theory into applied, job-ready skills. Whether you're learning for a career switch, academic research, or launching your own AI-powered product, this book gives you the clarity, code, and confidence to succeed. What You’ll Learn:Fundamentals of PyTorch 2.x: tensors, autograd, and model trainingBuilding CNNs with real datasets like MNIST and CIFAR-10Advanced projects in computer vision: Faster R-CNN, YOLO, and U-NetRNNs, LSTMs, and text classification with transformersModel deployment with TorchScript, TorchServe, and REST APIsPerformance tuning: mixed precision training, quantization, pruningReal mini-projects in medical imaging and natural language processing Why This Book?Up-to-date and beginner-friendly: Focused on PyTorch 2.x and real-world relevanceProject-based and practical: Build actual apps, not just toy examplesClear and concise: Every line of code is explained and designed to run out of the boxStart building deep learning models with confidence today. Whether you're training your first neural network or deploying to production, Deep Learning with PyTorch 2.x helps you get there faster.
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