Quick Start with PyTorch: Tensor Dimension Transformation and Common Operations
This article introduces the core knowledge of PyTorch tensors, including basics, dimension transformations, common operations, and exercise suggestions. Tensors are the basic structure for storing data in PyTorch, similar to NumPy arrays, and support GPU acceleration and automatic differentiation. They can be created using `torch.tensor()` from lists/numbers, `torch.from_numpy()` from NumPy arrays, or built-in functions to generate tensors of all zeros, ones, or random values. Dimension transformation is a key operation: `reshape()` flexibly adjusts the shape (keeping the total number of elements unchanged), `squeeze()` removes singleton dimensions, `unsqueeze()` adds singleton dimensions, and `transpose()`/`permute()` swap dimensions. Common operations include basic arithmetic operations, matrix multiplication with `matmul()`, broadcasting (automatic dimension expansion for operations), and aggregation operations such as `sum()`, `mean()`, and `max()`. The article suggests consolidating tensor operations through exercises, such as dimension adjustment, broadcasting mechanisms, and dimension swapping, to master the "shape language" and lay a foundation for subsequent model construction.
Read MoreLearning PyTorch from Scratch: A Beginner's Guide from Tensors to Neural Networks
This article introduces the core content and basic applications of PyTorch. Renowned for its flexibility, intuitiveness, and Python-like syntax, PyTorch is suitable for deep learning beginners and supports GPU acceleration and automatic differentiation. The core content includes: 1. **Tensor**: The basic data structure, similar to a multi-dimensional array. It supports creation from data, all-zero/all-one, random numbers, conversion with NumPy, shape operations, arithmetic operations (element-wise/matrix), and device conversion (CPU/GPU). 2. **Automatic Differentiation**: Implemented through `autograd`. Tensors with `requires_grad=True` will track their computation history, and calling `backward()` automatically computes gradients. For example, for the function \( y = x^2 + 3x - 5 \), the gradient at \( x = 2 \) is 7.0. 3. **Neural Network Construction**: Based on the `torch.nn` module, it includes linear layers (`nn.Linear`), activation functions, loss functions (e.g., MSE), and optimizers (e.g., SGD). It supports custom model classes and composition with `nn.Sequential`. 4. **Practical Linear Regression**: Generates simulated data \( y = 2x + 3 + \text{noise} \), defines a linear model, MSE loss,
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