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PyTorch Programming Playbook For Beginners: A Practical Guide to Tensors, Neural Networks, Model Training, and AI Development
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ISBN13: 9798171172947
Publisher: Independently Published
Pages: 214
Weight: 0.84
Height: 0.45 Width: 7.00 Depth: 10.00
Language: English
Publisher: Independently Published
Pages: 214
Weight: 0.84
Height: 0.45 Width: 7.00 Depth: 10.00
Language: English
PyTorch Programming Playbook For Beginners provides a practical introduction to PyTorch for readers who want to move beyond machine learning theory and start building real models with code.
The book begins with the foundations of PyTorch, explaining tensors, tensor operations, computational graphs, automatic differentiation, and the core concepts needed to understand how PyTorch works. It then progresses into neural networks, datasets, data loaders, loss functions, optimizers, training loops, validation, and model evaluation.
The book begins with the foundations of PyTorch, explaining tensors, tensor operations, computational graphs, automatic differentiation, and the core concepts needed to understand how PyTorch works. It then progresses into neural networks, datasets, data loaders, loss functions, optimizers, training loops, validation, and model evaluation.
Rather than treating PyTorch as a collection of disconnected APIs, the book develops an understanding of how the major components work together in a complete machine learning workflow. Readers learn how to prepare data, construct neural network architectures, train models, diagnose problems, improve performance, and organize PyTorch projects effectively.
What You Will Learn
What You Will Learn
- Understand PyTorch tensors and tensor operations
- Work with CPU and GPU computation
- Use automatic differentiation and autograd
- Build neural networks with torch.nn
- Prepare datasets and create data pipelines
- Implement training and validation loops
- Work with loss functions and optimizers
- Monitor and evaluate model performance
- Save and load trained models
- Identify and troubleshoot common training problems
- Build practical neural network projects
- Develop a foundation for more advanced deep learning with PyTorch
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