Specifying data types (#5893)

* add documentation for specifying data types in Pytorch tensors

* Updated tags.md and added new directory for specifying data types

* add main file to the branch

* Update specifying-data-types.md

minor fixes

* Update yarn.lock

* Update content/pytorch/concepts/tensor-operations/terms/specifiying-data-types/specifying-data-types.md

* Update content/pytorch/concepts/tensor-operations/terms/specifiying-data-types/specifying-data-types.md

* Update content/pytorch/concepts/tensor-operations/terms/specifiying-data-types/specifying-data-types.md

* Update content/pytorch/concepts/tensor-operations/terms/specifiying-data-types/specifying-data-types.md

* Fix file path

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content/pytorch/concepts/tensor-operations/terms/specifying-data-types/specifying-data-types.md

@@ -0,0 +1,69 @@
+---
+Title: 'Specifying Data Types'
+Description: 'Determines how tensors are stored and processed, impacting precision, memory usage, and computation speed.'
+Subjects:
+  - 'Data Science'
+  - 'Machine Learning'
+  - 'Deep Learning'
+Tags:
+  - 'Pytorch'
+  - 'Tensor'
+  - 'Data Types'
+CatalogContent:
+  - 'learn-Intro-to-PyTorch-and-Neural-Networks'
+  - 'paths/data-science'
+---
+
+In PyTorch, specifying the data types for [`tensors`](https://www.codecademy.com/resources/docs/pytorch/tensors) is crucial as they are the core data structures used to store and process data. Each tensor's data type (`dtype`) defines the kind of values it holds (e.g., `integer`, `float`, `boolean`), ensuring precision, improving performance, and maintaining compatibility during computations.
+
+## Syntax
+
+To specify a data type in a PyTorch tensor, use the `dtype` parameter when creating a tensor or the `.to()` method for converting an existing one.
+
+### For specifying `dtype` when creating a tensor
+
+```pseudo
+torch.tensor(data, dtype=torch.<data_type>)
+```
+
+- `data`: The input data used to create the tensor. This can be a list, NumPy array, or another tensor.
+- `dtype`: Specifies the data type of the tensor. Common data types include:
+  - `torch.float32` (default): 32-bit floating-point
+  - `torch.float64`: 64-bit floating-point
+  - `torch.int32`: 32-bit integer
+  - `torch.int64`: 64-bit integer
+  - `torch.bool`: Boolean
+
+### For converting an existing tensor to a different data type
+
+```pseudo
+tensor.to(torch.<data_type>)
+```
+
+## Example
+
+In the example below a tensor is created with a specified data type, another with a different type, and one tensor is converted to a new data type:
+
+```py
+import torch
+
+# Creating a float32 tensor
+float_tensor = torch.tensor([1.0, 2.0, 3.0], dtype=torch.float32)
+print(float_tensor)
+
+# Creating an int64 tensor
+int_tensor = torch.tensor([1, 2, 3], dtype=torch.int64)
+print(int_tensor)
+
+# Converting a tensor to a different data type
+converted_tensor = float_tensor.to(torch.int64)
+print(converted_tensor)
+```
+
+The code above generates the output as:
+
+```shell
+tensor([1., 2., 3.])
+tensor([1, 2, 3])
+tensor([1, 2, 3])
+```

documentation/tags.md

@@ -336,6 +336,7 @@ Tags
 Target
 Technical Interviews
 Templates
+Tensor
 TensorFlow
 Text-To-Image
 Text Processing