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10 items

Writes › Book › Deep Learning with PyTorch › 01 › 02 ›

Scalars, Vectors, Matrices, and Tensors

Deep learning represents data and computation using arrays of numbers.
deep-learning pytorch
Writes › Book › Deep Learning with PyTorch › 01 › 02 ›

Tensor Shapes, Dimensions, and Memory Layout

Deep learning systems manipulate tensors with millions or billions of numerical entries.
deep-learning pytorch
Writes › Book › Deep Learning with PyTorch › 01 › 02 ›

Tensor Creation and Initialization

Neural networks start with tensors. Some tensors come from data.
deep-learning pytorch
Writes › Book › Deep Learning with PyTorch › 01 › 02 ›

Tensor Arithmetic and Broadcasting

Tensor arithmetic is the basic computation layer of PyTorch.
deep-learning pytorch
Writes › Book › Deep Learning with PyTorch › 01 › 02 ›

Indexing, Slicing, and Tensor Views

Indexing and slicing select parts of a tensor. These operations are used constantly in PyTorch: selecting batches, cropping images, extracting token positions, applying masks, gathering logits, and rearranging model
deep-learning pytorch
Writes › Book › Deep Learning with PyTorch › 01 › 02 ›

Matrix Operations

Matrix operations are the main arithmetic language of deep learning.
deep-learning pytorch
Writes › Book › Deep Learning with PyTorch › 01 › 02 ›

Random Tensor Generation

Random tensors are used throughout deep learning. They initialize parameters, shuffle examples, sample noise, apply dropout, augment data, and generate outputs from probabilistic models.
deep-learning pytorch
Writes › Book › Deep Learning with PyTorch › 01 › 02 ›

Tensor Data Types and Devices

A tensor has values, shape, data type, and device placement.
deep-learning pytorch
Writes › Book › Deep Learning with PyTorch › 01 › 02 ›

Tensor Memory Layout and Performance

A tensor has a logical shape and a physical memory layout. The shape tells us how to interpret the tensor as an array. The memory layout tells us how the entries are stored in memory.
deep-learning pytorch
Writes › Book › Deep Learning with PyTorch › 01 › 02 ›

CPU and GPU Tensors

PyTorch tensors live on devices. A device is the hardware location where tensor storage exists and where tensor operations execute.
deep-learning pytorch