15th November, 2024

Hey Everyone! 👋 Thanks for stopping by on this blog series on learning PyTorch.

I have been a great admirer of TensorFlow from a long time as it was the first Deep learning framework I learnt. And first ones are always special!

But if I am already well-versed in TensorFlow, why take the headache of learning a completely new framework?

To be honest that’s a valid question and I have the answer to that: curiosity and necessity.

Curiosity to learn PyTorch was there for a long time and hence I finally decided to take it up and dive into it.
Necessity to understand, comprehend and implement the latest advancements happening in GenAI space. The super fast paced advancements in LLM and GenAI space is like crazy and to understand the work done through research, PyTorch is the number one choice as most of them are implemented using this framework.

Now that I have justified why I am diving into PyTorch, it’s time to begin with the very first part of this series.

I will be following some combination of resources throughout and will be listing all of them at the end in Resources section for anyone to refer.

Let’s get right into PyTorch!! 🥳

The Why?

In order to understand about anything, questioning the why of it is really crucial. You should be absolutely clear why you are studying this and why even this thing exists in first place!

Answering the question in context of PyTorch, we also have a very valid reason why PyTorch came into existence.

The beginning of its journey began in 2002 (well that’s the year I was born too) with the development of Torch.

Torch is a framework that allowed handling Tensor based mathematical operations seamlessly on Graphical Processing Units (GPUs) leading to efficiency and enhanced performance. It was written in Lua programming language which is not so popular.

So Torch and PyTorch are similar?

Well not really! I also had this confusion initially but this is not correct. Meta AI researchers and scientists realised that Torch is a really helpful framework and can be heavily used for Deep Learning use cases that involved heavy tensor based mathematical operations. But the only friction holding it back was the language it was written in, Lua.