diff --git a/README.md b/README.md
index 46f5dcd..4c08715 100644
--- a/README.md
+++ b/README.md
@@ -10,7 +10,7 @@ working across different projects via [VisualMode](https://www.visualmode.dev/).
 
 For a steady stream of TILs, [sign up for my newsletter](https://visualmode.kit.com/newsletter).
 
-_1863 TILs and counting..._
+_1864 TILs and counting..._
 
 See some of the other learning resources I work on:
 
@@ -1105,6 +1105,7 @@ If you've learned something here, support my efforts writing daily TILs by
 - [Get Absolute Seconds From `timedelta` Object](python/get-absolute-seconds-from-timedelta-object.md)
 - [Get Quotient And Remainder In One Operation](python/get-quotient-and-remainder-in-one-operation.md)
 - [Globally Install CLI Tool With UV](python/globally-install-cli-tool-with-uv.md)
+- [Initialize A PyTorch Tensor](python/initialize-a-pytorch-tensor.md)
 - [Install With PIP For Specific Interpreter](python/install-with-pip-for-specific-interpreter.md)
 - [Iterate First N Items From Enumerable](python/iterate-first-n-items-from-enumerable.md)
 - [Iterate Over A Dictionary](python/iterate-over-a-dictionary.md)
diff --git a/python/initialize-a-pytorch-tensor.md b/python/initialize-a-pytorch-tensor.md
new file mode 100644
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@@ -0,0 +1,81 @@
+# Initialize A PyTorch Tensor
+
+A _tensor_ is an n-dimensional array that takes on a specified shape and holds
+scalars, typically floats, in each of its positions.
+[PyTorch](https://pytorch.org/) has a comprehensive suite of tools for working
+with tensors. To work with tenors, the first thing I need to do is initialize
+one. Here are a handful of ways to create a 2-dimensional tensor (a matrix)
+depending on various needs.
+
+Here is a 3x4 matrix full of zeros:
+
+```python
+>>> torch.zeros(torch.Size((3, 4)))
+tensor([[0., 0., 0., 0.],
+        [0., 0., 0., 0.],
+        [0., 0., 0., 0.]])
+```
+
+Here is a 3x4 matrix full of ones:
+
+```python
+>>> torch.ones(3,4)
+tensor([[1., 1., 1., 1.],
+        [1., 1., 1., 1.],
+        [1., 1., 1., 1.]])
+```
+
+And here is a 3x4 matrix full of a specific other value:
+
+```python
+>>> torch.full((3,4), 13.0)
+tensor([[13., 13., 13., 13.],
+        [13., 13., 13., 13.],
+        [13., 13., 13., 13.]])
+```
+
+PyTorch is very flexible. I can specify the shape of the `tensor` with
+positional arguments or a tuple for the dimensions. I can even construct a
+`torch.Size` object.
+
+Here is an arguably more useful example where the matrix is seeded with random
+values in the range `[0,1)` using
+[`torch.rand`](https://docs.pytorch.org/docs/2.13/generated/torch.rand.html):
+
+```python
+>>> torch.rand(torch.Size((3,4)))
+tensor([[0.4148, 0.8045, 0.3093, 0.3363],
+        [0.0120, 0.7161, 0.1108, 0.5510],
+        [0.4805, 0.9430, 0.2852, 0.0966]])
+```
+
+These could be used as starting weights in a training process that then get
+tweaked over time.
+
+There are other random tensor functions like
+[`torch.randint`](https://docs.pytorch.org/docs/2.13/generated/torch.randint.html)
+and
+[`torch.randn`](https://docs.pytorch.org/docs/2.13/generated/torch.randn.html).
+
+How about a [random permutation](https://docs.pytorch.org/docs/2.13/generated/torch.randperm.html)
+of integers `[0,12)` reshaped into a 3x4 matrix:
+
+```python
+>>> torch.randperm(12).reshape(3,4)
+tensor([[ 0,  3,  1, 11],
+        [ 7,  8,  9,  4],
+        [10,  5,  6,  2]])
+```
+
+And though there are many other ways to initialize a tensor, the last one I will
+show is
+[`torch.eye`](https://docs.pytorch.org/docs/2.13/generated/torch.eye.html) which
+creates a matrix with ones down the diagonal.
+
+```python
+>>> torch.eye(4)
+tensor([[1., 0., 0., 0.],
+        [0., 1., 0., 0.],
+        [0., 0., 1., 0.],
+        [0., 0., 0., 1.]])
+```