Summary  
This chapter covers built-in functions for directly creating tensors with specified values or shapes—such as tensors of zeros or ones, evenly spaced sequences, and tensors matching the shape of an existing tensor.  

General domain of usage  
Deep learning model parameter initialization

Similarly to **NumPy**, PyTorch also offers several built-in functions to **create tensors directly**. These functions help in initializing data placeholders and generating structured or custom tensors.

## Tensor of Zeros and Ones

To create a tensor filled with **zeros**, use `torch.zeros()`. The arguments represent the **size of each dimension**, with the number of arguments corresponding to the **number of dimensions**:

import torch 
tensor = torch.zeros(4, 2)
print(tensor)

This is useful for **initializing biases** or placeholders where the initial values are set to zero. Similarly, use `torch.ones()` to create a tensor filled with **ones**:

import torch
tensor = torch.ones(3, 3)
print(tensor)

This can be particularly useful for **initializing weights**, **bias terms**, or performing operations where a tensor of ones serves as a neutral element or a specific multiplier in mathematical computations.

## Arange and Linspace

Similarly to `numpy.arange()`, `torch.arange()` generates a sequence of values with a specified **step size**:

import torch
tensor = torch.arange(0, 10, step=2)
print(tensor)

We have successfully created a tensor with values from `0` to `10` **exclusive** with the step size equal to `2`. To create **evenly spaced** values between a start and end point, use `torch.linspace()`:

import torch
tensor = torch.linspace(0, 1, steps=5)
print(tensor)

This generates a tensor with `5` **equally spaced** values between `0` and `1` **inclusive**.

## Tensor from Shape

You can create tensors with a specific shape by using the **"like" variants** of creation functions. These create tensors with the **same shape** as an existing tensor:

import torch
x = torch.tensor([[1, 2, 3], [4, 5, 6]])
zeros_tensor = torch.zeros_like(x)
ones_tensor = torch.ones_like(x)
print(f"Tensor of zeros: {zeros_tensor}")
print(f"Tensor of ones: {ones_tensor}")

What will be the output of the following PyTorch code snippet?

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.

Tensor Creation Functions

Tensor of Zeros and Ones

Arange and Linspace

Tensor from Shape