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Updated 2 years ago
Tensors
Summary and Further Reading
Try out this assignment to learn more about tensor operations in PyTorch: https://jovian.ai/aakashns/01-tensor-operations
This tutorial covers the following topics:
- Introductions to PyTorch tensors
- Tensor operations and gradients
- Interoperability between PyTorch and Numpy
You can learn more about PyTorch tensors here: https://pytorch.org/docs/stable/tensors.html.
The material in this series is inspired by:
- PyTorch Tutorial for Deep Learning Researchers by Yunjey Choi
- FastAI development notebooks by Jeremy Howard.
With this, we complete our discussion of tensors and gradients in PyTorch, and we're ready to move on to the next topic: Gradient Descent & Linear Regression.
At its core, PyTorch is a library for processing tensors. A tensor is a number, vector, matrix, or any n-dimensional array. Let's create a tensor with a single number.
import torch
# Number
t1 = torch.tensor(4.)
t1
tensor(4.)