Random tensors are useful for initializing data or **weights  in machine learning models** (the most often use case). Let's dive into the different ways to create them.

## Random Uniform Tensors
The `torch.rand()` function is used to create a tensor with random values drawn from a **uniform distribution** between `0` and `1`. Similarly to the `zeros()` and `ones()`functions, the arguments specify the **shape** of the tensor.

import torch
# Create a 6x8 tensor with random values between 0 and 1
random_tensor = torch.rand(6, 8)
print(random_tensor)

## Random Normal Tensors
The `torch.randn()` function is used to create a tensor with random values drawn from a **standard normal distribution** (mean = 0, standard deviation = 1).

# Create a 2x2 tensor with random values from a normal distribution
normal_tensor = torch.randn(2, 2)
print(normal_tensor)

## Random Integer Tensors
The `torch.randint()` function is used to create a tensor with **random integer** values drawn from **discrete uniform distribution**. 

The first two parameters of this functions (`low`, which is equal to `0` by default, and `high`) specify the range of values (from `low` to `high` **exclusive**). The next parameter specifies the **shape** of the tensor as a **tuple**.


# Create a 4x3 tensor with random integers between 0 (inclusive) and 10 (exclusive)
integer_tensor = torch.randint(0, 10, (4, 3))
print(integer_tensor)

## Setting Random Seed
To ensure **reproducibility**, you can set a manual seed. This fixes the random numbers generated so they are the same **each time you run the code**.

# Set the random seed
torch.manual_seed(42)
# Create a 2x3 tensor with random values
seeded_tensor = torch.rand(2, 3)
print(seeded_tensor)

## Practical Use Cases for Random Tensors

- **Weight initialization**: random tensors are often used to initialize weights in neural networks;
- **Simulating data**: generate random datasets for testing and experimentation;
- **Random sampling**: use random tensors for tasks like dropout and noise addition in models.

Neural Networks with PyTorch

Creating Random Tensors

Random Uniform Tensors

Random Normal Tensors

Random Integer Tensors

Setting Random Seed

Practical Use Cases for Random Tensors