Summary  
This chapter shows how to generate random tensors using uniform, normal, and integer distributions and how to set a manual seed for reproducible results in PyTorch.  

General domain of usage  
machine learning

Random tensors are useful for initializing data or **weights  in machine learning models** (the most common use case).

## 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).

import torch
# 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**.


import torch
# Create a 4x3 tensor with random integers between 0 and 10
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**.

import torch
# 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.

Which of the following statements about random tensors in PyTorch is correct?

Learn the fundamental and advanced concepts needed to work with PyTorch efficiently. Gain a solid understanding tensors, including creation, operations, and reshaping. Explore the essentials of gradients, backpropagation, and linear regression before moving on to handling datasets. Master the skill needed build, train, and evaluate neural networks.

Creating Random Tensors

Random Uniform Tensors

Random Normal Tensors

Random Integer Tensors

Setting Random Seed

Practical Use Cases for Random Tensors