Articoli correlati a Learn all about PyTorch
- EditoreIndependently published
- Data di pubblicazione2023
- ISBN 13 9798393438241
- RilegaturaCopertina flessibile
- LinguaInglese
- Numero di pagine123
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Learn all about PyTorch
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Learn all about PyTorch (Paperback)
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Paperback. Condizione: new. Paperback. Learn all about PyTorch PyTorch is a popular open-source machine learning framework developed by Facebook's artificial intelligence research team. It is based on the Torch library, which is a scientific computing framework that is widely used in machine learning research. PyTorch is designed to be a flexible and user-friendly platform for building and training machine learning models, particularly in the areas of computer vision, natural language processing, and speech recognition. At its core, PyTorch is built around the concept of tensors, which are multi-dimensional arrays that can be used to represent both data and models. These tensors are the basic building blocks of PyTorch, and all computations in PyTorch are performed using tensors. One of the key features of PyTorch is its dynamic computational graph, which allows for efficient computation and easy debugging. This means that PyTorch models can be defined and modified on the fly during training, allowing for greater flexibility and experimentation. The book covers the following: 1 Introduction to PyTorchWhat is PyTorch?Why use PyTorch?Overview of PyTorch features 2 Getting Started with PyTorchInstalling PyTorchPyTorch basics: Tensors, operations, and variablesBuilding your first PyTorch model 3 Data Preparation with PyTorchData loading and preprocessingDataset and DataLoader classesData augmentation 4 Building Machine Learning Models with PyTorchLinear regression with PyTorchLogistic regression with PyTorchNeural networks with PyTorchConvolutional neural networks with PyTorchRecurrent neural networks with PyTorchGenerative models with PyTorch 5 Training and Evaluating PyTorch ModelsLoss functions in PyTorchOptimizers in PyTorchOverfitting and underfittingEvaluation metricsHyperparameter tuning 6 Advanced Topics in PyTorchTransfer learning with PyTorchReinforcement learning with PyTorchNatural language processing with PyTorchTime series analysis with PyTorchDistributed training with PyTorch 7 Deploying PyTorch ModelsExporting PyTorch models for productionServing PyTorch models with Flask and other web frameworksIntegrating PyTorch models into mobile applications 8 Best Practices for PyTorch DevelopmentPyTorch code organizationDebugging PyTorch modelsTesting PyTorch modelsOptimizing PyTorch models for performance 9 PyTorch in the Real World: Case Studies and ApplicationsSuccessful PyTorch implementations in industryChallenges and limitations of using PyTorch in production environmentsBest practices for using PyTorch in production environments 10 Future of PyTorchPyTorch roadmap and upcoming featuresComparison with other machine learning frameworksCommunity and resources for PyTorch users Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Codice articolo 9798393438241
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