5 Simple Functions to get started with PyTorch

PyToarch is an open-source machine learning library, which is primarily developed by Facebook's AI Research Lab. It has a wide array of applications, ranging from Computer Vision to Natural Language Processing etc.
In this notebook, a few important functions from the library is introduced, that helps with creating and modifying Tensors.

torch.linspace
torch.zeros_like
torch.reshape
torch.split
torch.stack

Before we begin, let's install and import PyTorch

#@title
# Uncomment and run the appropriate command for your operating system, if required

# Linux / Binder
# !pip install numpy torch==1.7.0+cpu torchvision==0.8.1+cpu torchaudio==0.7.0 -f https://download.pytorch.org/whl/torch_stable.html

# Windows
# !pip install numpy torch==1.7.0+cpu torchvision==0.8.1+cpu torchaudio==0.7.0 -f https://download.pytorch.org/whl/torch_stable.html

# MacOS
# !pip install numpy torch torchvision torchaudio

#@title
# Import torch and other required modules
import torch

Function 1 - torch.linspace

This is one of the most important function if you have to create a range of data.
Let us say you want a Tensor populated by integers from 1 to 10. Instead of typing them one by one like,

torch.tensor([1.,2.,3.,4.,5.,6.,7.,8.,9.,10.])

you can just use the torch.linspace() to make one.
It takes the arguments, starting value, last value and the number of elements needed in the tensor.

# Example 1 - working
torch.linspace(1,10,10)

tensor([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.])