From the day you stepped foot into a classroom to the day when you graduated from a university with a computer science degree in your hand, you always wanted to create something unique. You have seen technology evolve in front of your eye s and you also want to write your name in history books by creating something that could become the next Facebook.

Despite being halfway through your computer science degree and after learning half a dozen programming languages, you are still struggling to decide which programming language should you make a career in. Does all that sound familiar? If yes, then you are not alone. Most developers have been in such a situation at least once in their lifetime, irrespective of whether you are a web developer creating websites or a app developer conducting app development.

A closer look at rankings of programming languages can clear all the confusion and give you the answer to this question. Python is one of the fastest-growing languages according to the popularity of the programming language index with a 19% growth rate. According to the TIOBE index, Python ranks in the third position but with the pace at which its popularity is increasing it will be about the time when it will surpass Java to capture the second spot.

Why Developers Should Choose Python?

Apart from its popularity as a programming language, Python offers tons of advantages to developers. Some of them are as follows:

• Python is easy to learn and has a small learning curve
• Python is free and open-source
• Python offers portability
• Python gives developers a variety of frameworks and libraries to choose from
• Python has a large community of developers to support new developers
• Python offers developers more flexibility as compared to other programming languages
• Python has its own integrated development environment

If you are a developer who is working on a machine learning project and want to drive it to success then, you should choose the best Python libraries for machine learning. That is where PyTorch comes into play.

What is PyTorch?

PyTorch is a relatively new and loose part of the Torch library in Python. Its ability to handle dynamic computation graph gives it a clear advantage over other Python libraries. Backed by Facebook Artificial Intelligence Research Team, you can expect some great things from this python library and it does not let you down.

With its smoother integration and user-friendly APIs, developers can create computation graphs without breaking a sweat. Combine that with multi-GPU support, custom data loaders, easy to use preprocessors and you get a python library that checks the most boxes. 

Here are five useful PyTorch functions you can use in your next machine learning projects.

1. Torch.Linespace

Want to create an equally spaced tensor between the start and end? That is where this function can come in handy. The size of the tensor can be defined by using the steps perimeter. By default, the tensor size is 100 which means that steps=100.

torch.linspace(1, 10)

Here is what the output of this function will look like.

tensor([ 1.0000, 1.0909, 1.1818, 1.2727, 1.3636, 1.4545, 1.5455, 1.6364, 1.7273, 1.8182, 1.9091, 2.0000, 2.0909, 2.1818, 2.2727, 2.3636, 2.4545, 2.5455, 2.6364, 2.7273, 2.8182, 2.9091, 3.0000, 3.0909, 3.1818, 3.2727, 3.3636, 3.4545, 3.5455, 3.6364, 3.7273, 3.8182, 3.9091, 4.0000, 4.0909, 4.1818, 4.2727, 4.3636, 4.4545, 4.5455, 4.6364, 4.7273, 4.8182, 4.9091, 5.0000, 5.0909, 5.1818, 5.2727, 5.3636, 5.4545, 5.5455, 5.6364, 5.7273, 5.8182, 5.9091, 6.0000, 6.0909, 6.1818, 6.2727, 6.3636, 6.4545, 6.5455, 6.6364, 6.7273, 6.8182, 6.9091, 7.0000, 7.0909, 7.1818, 7.2727, 7.3636, 7.4545, 7.5455, 7.6364, 7.7273, 7.8182, 7.9091, 8.0000, 8.0909, 8.1818, 8.2727, 8.3636, 8.4545, 8.5455, 8.6364, 8.7273, 8.8182, 8.9091, 9.0000, 9.0909, 9.1818, 9.2727, 9.3636, 9.4545, 9.5455, 9.6364, 9.7273, 9.8182, 9.9091, 10.0000])

2. Torch.eye

If you want to create a two-dimensional tensor with two diagonals having value 0 and 1 respectively, you should use Torch.eye function. The function accepts two perimeters known as n and m.

torch.eye(n=4, m=5)

Here is the output of this function

tensor([[1., 0., 0., 0., 0.],

       [0., 1., 0., 0., 0.],

       [0., 0., 1., 0., 0.],

       [0., 0., 0., 1., 0.]])

3. Torch.full

This function returns a tensor of size with the values filled with fill_value. Here size can either be a list or tuple.

torch.full(size=(3,2), fill_value=10)

The function will return this output.

tensor([[10., 10.],

       [10., 10.],

       [10., 10.]])

4. Torch.cat

If you want to connect a series of tensors over a dimension, Torch.cat function help you achieve that goal. Remember that all the tensors will be of same shape.\

= torch.ones(3,2)

b = torch.zeros(3,2)

torch.cat((a, b)) # default dim=0

Here is what the output would look like after executing this function

tensor([[1., 1.],

       [1., 1.],

       [1., 1.],

       [0., 0.],

       [0., 0.],

       [0., 0.]])

5. Torch.take

If you want tensor elements at particular indices, Torch.take function can come in handy. This function returns a tensor with elements of tensor at specific indices. Do keep in mind that this function will consider input tensor as ID tensor in order to return the desired values.

# 1D input Tensor

b = torch.tensor([10, 20, 30, 40, 50])

torch.take(b, torch.tensor([2]))

You will get this output after running this function

tensor([30])

Which PyTorch function do you use most often and why? Let us know in the comments section below.

Author Bio:Irfan Ak is an experienced digital & content marketing strategist at Branex, a pro mobile app development company. He is a regular contributor on various websites. He has worked with several brands and created value for them.