Creation of the logspace entry (Codecademy#5327)

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content/pytorch/concepts/tensors/terms/logspace/logspace.md

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+---
+Title: '.logspace()'
+Description: 'Returns a one-dimensional tensor with values logarithmically spaced.'
+Subjects:
+  - 'Data Science'
+  - 'Machine Learning'
+Tags:
+  - 'AI'
+  - 'Data Types'
+  - 'Deep Learning'
+  - 'Functions'
+CatalogContent:
+  - 'intro-to-py-torch-and-neural-networks'
+  - 'py-torch-for-classification'
+---
+
+The **`.logspace()`** function returns a one-dimensional tensor with values logarithmically spaced.
+
+The function is useful for generating logarithmically spaced values for various applications, such as plotting data on a logarithmic scale or creating logarithmic scales for neural network hyperparameters.
+
+## Syntax
+
+```pseudo
+torch.logspace(start, end, steps, base, dtype=None)
+```
+
+- `start`: The first number in the range expressed as a logarithm.
+- `end`: The last number in the range expressed as a logarithm.
+- `steps`: Number of elements to be returned in the tensor.
+- `base`: The base of the logarithm used for calculating the values default value is 10.
+- `dtype`: Specifies the data type of the returned tensor.
+
+## Example 1
+
+In this example, the code generates a tensor containing 5 logarithmically spaced values between 1 and 1000:
+
+```py
+import torch
+
+# Generate a tensor with 5 logarithmically spaced values between 1 and 1000
+tensor = torch.logspace(0, 3, steps=5)
+print(tensor)
+```
+
+The code above generates the following output:
+
+```shell
+tensor([   1.0000,    5.6234,   31.6228,  177.8279, 1000.0000])
+```
+
+## Example 2
+
+In this example, the code generates a tensor containing 3 logarithmically spaced values between 1 and 10 using the `.logspace()` function:
+
+```py
+import torch
+
+# Generate a tensor with 3 logarithmically spaced values between 0 and 10
+tensor = torch.logspace(0, 1, steps=3, dtype=torch.float64)
+print(tensor)
+```
+
+Output:
+
+```shell
+tensor([1.0000, 3.1623, 10.0000], dtype=torch.float64)
+```
+
+In this example, we created a tensor `tensor` containing 3 logarithmically spaced values between 0 and 1 using the `.logspace()` function with a data type of `torch.float64`. The tensor `tensor` contains the values `[1.0000, 3.1623, 10.0000]`.