PyTorch Archives - Python Lore

Saving and Loading Models with torch.save and torch.load

April 22, 2024Python modules, PyTorchModel Persistence, torch.load, torch.save

Master saving and loading models with torch.save and torch.load in PyTorch. Learn how to serialize models, including architecture, hyperparameters, and training details. Discover the importance of model serialization for sharing, reusing, and deploying models in machine learning and deep learning projects.

Python modules PyTorch

Data Loading and Processing using torch.utils.data

March 27, 2024Python modules, PyTorchData Loading, torch.utils.data

Easily load and process data for machine learning models with torch.utils.data in PyTorch. Utilize Dataset and DataLoader classes to efficiently handle datasets, manage batching, shuffling, and parallel loading. Simplify data preparation for training or inference tasks with these powerful tools.

Python modules PyTorch

Dynamic Computation Graphs and torch.autograd.Function

March 1, 2024Python modules, PyTorchDynamic Computation Graphs

Dynamic computation graphs in PyTorch, like torch.autograd.Function, offer flexibility in constructing and executing graphs on-the-fly. This allows for dynamic changes, conditional execution, and recursive functions, aligning closely with how programmers think. Customize your neural networks with dynamic computation graphs for a more intuitive approach.

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Convolutional Neural Network Layers in torch.nn

February 4, 2024Python modules, PyTorchConvolutional Layers, torch.nn

Explore the layers of Convolutional Neural Networks (CNNs) in torch.nn. Learn how CNNs process and analyze visual data, utilize convolutional and pooling layers, and employ fully connected layers for high-level reasoning. See an example of a simple CNN model in action.

Python modules PyTorch

Autograd: Automatic Differentiation with torch.autograd

January 9, 2024Python modules, PyTorchAutomatic Differentiation, torch.autograd

Autograd is a powerful tool in PyTorch for automatic differentiation, allowing developers to compute gradients for tensor operations effortlessly. This technology optimizes machine learning models by handling derivative calculations, enabling developers to focus on designing neural network architectures and defining loss functions.

Python modules PyTorch

Introduction to PyTorch Tensors with torch.Tensor

January 7, 2024Python modules, PyTorchTensors, torch.Tensor

PyTorch Tensors are crucial for encoding model inputs, outputs, and parameters. Learn how to create and initialize tensors with PyTorch's torch.Tensor class, and discover their advantages over NumPy arrays, including GPU acceleration. Harness the power of PyTorch for machine learning.