PyTorch- A Blessing!

Hello Everyone! This is my fifth writing on my journey of Completing the Deep Learning Nanodegree in a month! So far, in these 6 days, I’ve completed two out of six modules of the Nanodegree and trust me, its quite alot to learn!

Day- 6

So, today’s lesson name is “Deep Learning with PyTorch” and just as the name suggests, in this lesson, we’ll be implementing all that we’ve learnt before with the help of PyTorch.

PyTorch

First, lets talk about what PyTorch is. I’m sure there are many definitions on the web but from a beginner developer’s view, its gold. What I mean by that is, all the mathematics that I’ve studied so far, for Gradient Descent, Feed Forward, Back Propagation, Activation Functions, etc, etc are good to know and will definitely be compulsory if you’re looking to be renown in the ML industry, but if you’re someone who is a beginner and wants to get to the good stuff, the models and accuracy, etc etc, or you’re a ML Guru but don’t want to write all the equations and each line by ourself, most of the professionals use Libraries as well though, then PyTorch is what you’re looking for. Its basically an Open Source Library which has almost all the major functions that are needed to be performed to train a model in it. Lets take examples:

• Network Architecture