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portada Hands-On PyTorch for AI and Machine Learning 2026. A Practical Guide to Building Smart Models, Training Neural Networks, and Deploying Deep Learning Solutions with Python
Type
Physical Book
Publisher
Author
Year
2026
Language
English
Pages
204
Format
Paperback
Dimensions
22.9x15.2x1 cm
ISBN13
9798253051085

Hands-On PyTorch for AI and Machine Learning 2026. A Practical Guide to Building Smart Models, Training Neural Networks, and Deploying Deep Learning Solutions with Python

Adrian M Kessler (Author) · Independently published · Paperback

Hands-On PyTorch for AI and Machine Learning 2026. A Practical Guide to Building Smart Models, Training Neural Networks, and Deploying Deep Learning Solutions with Python - Adrian M Kessler

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Synopsis "Hands-On PyTorch for AI and Machine Learning 2026. A Practical Guide to Building Smart Models, Training Neural Networks, and Deploying Deep Learning Solutions with Python"

Are you a developer, data scientist, or aspiring AI engineer looking to take your skills to the next level with PyTorch? Hands-On PyTorch for AI and Machine Learning 2026 is your ultimate guide to mastering modern deep learning using the most flexible and research-backed framework available today.
This isn't just another machine learning book-it's a practical, project-driven blueprint that walks you through every critical step of designing, building, training, and deploying neural networks using Python and PyTorch. Whether you're transitioning from TensorFlow, starting from scratch, or seeking a real-world playbook for AI, this guide is for you.
Inside, you'll learn how to:Understand the fundamentals of AI, machine learning, and deep learning in plain EnglishWork with tensors, autograd, and dynamic computation graphs like a proBuild your first neural network from scratch using torch.nn and SequentialTrain models with optimizers like SGD, Adam, and RMSProp, and fine-tune hyperparametersDevelop powerful CNNs for image classification and apply them to datasets like MNIST and CIFAR-10Dive into natural language processing with RNNs, GRUs, LSTMs, and Transformer architecturesUse pretrained models from torchvision.models and Hugging Face for transfer learningCreate custom datasets, implement data loaders, and write robust preprocessing pipelinesEvaluate your models with precision, recall, F1-score, and visualize performance using TensorBoard or Weights & BiasesDeploy models using Flask, FastAPI, or ONNX-and integrate them into mobile or web appsLeverage PyTorch Lightning to write cleaner, scalable, and production-ready codeStay ahead of the curve with future trends like AutoML, edge AI, quantization, and responsible AI practicesWhat sets this book apart:Future-focused for 2026 and beyond-updated tools, trends, and deployment practicesCode-first, no-fluff approach with real projects and clean architectureWritten for clarity-ideal for developers, ML engineers, and anyone transitioning into AIIncludes practical exercises and deployable templates for career-ready skillsApplicable across industries: healthcare, finance, cybersecurity, robotics, and moreWhether you're building a career in AI, optimizing your production pipelines, or simply want to stay relevant in the era of intelligent software, this book is your hands-on companion.
Perfect for learners at any level ready to build deep learning models that actually work.

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