The Top 4 Reasons to Learn PyTorch (and start getting into AI)

OpenAI, Tesla and Meta use PyTorch to power their machine learning products, perhaps you should too.

What it feels like writing PyTorch code. Photo by Sam Bourke.

Machine learning and artificial intelligence (AI) are exploding!

And as you’ll find out, PyTorch is one of the biggest driving forces behind many of the latest AI revolutions.

So if you’re looking to build machine learning and AI-powered systems, chances are you’ll eventually be stumbling across PyTorch.

But before we start with why you should learn PyTorch, let’s discuss what it is.

What is PyTorch?

PyTorch is an open-source Python-based framework for machine learning.

Open-source means anyone around the world can download or contribute (with adequate checks) to the PyTorch source code, which currently has over 60,000 stars on GitHub.

Many of the world’s most impressive machine learning and artificial intelligence (AI) models are built using PyTorch.

By building, I mean, the algorithms are coded in PyTorch and then are used for various tasks such as:

• Computer vision (such as the vision systems in self-driving cars)
• Natural language processing (such as performing classification on certain kinds of texts)
• Speech-to-text and text-to-speech
• Text-to-image generation (such as the one below)
• Time series forecasting and analysis

Image created with a text-to-image generation model built with PyTorch by OpenAI called DALL•E 2. Text used: “an artificial intelligence system being born out of a computer with flames coming out of it”. Source: OpenAI Labs.

And more!

Chances are if there’s a machine learning task you can think of, there’s a PyTorch model out there for it.

Note: I use the terms artificial intelligence (AI), machine learning (ML) and deep learning interchangeably. Deep learning is a form of ML and ML is a form of AI. PyTorch is mostly known for deep learning, the form of ML used for many of the latest AI advancements.

Why PyTorch?