# My 4th Attempt at AI/ML

So this was **my 4th attempt** at it. I come from a development background, so it was more of a learning by doing/coding thing.

![](https://miro.medium.com/v2/resize:fit:875/1*cpu8l60UM9LkzpQuSOn-LQ.png align="left")

I was always good at maths (or so I think), and it made sense to me — the concepts, proofs, the language of maths itself. So, I decided to jump into what this field has to offer, as they say it had a bunch of maths. And yes, it was a bunch of maths and theory, which was very opposite of what I did as a developer. Paper-pen with concepts learning was the key, and intuition to what exactly and how exactly things (regression/classification, models, tensors, ANN, CNN, RNN) worked as they are.

So 1st attempt was in my 2nd year I guess. It was from Coursera’s **Andrew NG’s** course. TBH, I was just parrot-learning, making Notion notes, and because even if I understood the maths of schools and colleges, it was quite different when it was not on paper and done in head (as dev habit). I was quite overwhelmed, and I convinced myself that I knew what I was learning until I did not. Soon, of course, the spark was all gone. I, as almost many people do, dropped it.

Fast forward, I went on YouTube learning through tutors like **Krish Naik**, 100 days of ML, **CampusX** (*he is the goat, btw*) 100 days of DL. At this point, I think I made myself comfortable with all the jargons in this field, but still it wasn’t clicking as it should (resistance free).

Press enter or click to view image in full size

![](https://miro.medium.com/v2/resize:fit:875/1*yQn4kldwUcqn0vympUdo5g.jpeg align="left")

Then I had subjects like ***Numerical Methods*** in 4th sem I guess, which felt somewhat similar to what I’d studied in classical ML. Then came 5th sem, the foundational subject “***Probability and Statistics.***” I had learned fundamental concepts like probability distributions, random variables, central limit theorem, and it was quite fascinating.

Press enter or click to view image in full size

![](https://miro.medium.com/v2/resize:fit:875/1*1ony-7eDE6u9TxHha22ifQ.jpeg align="left")

Finally, after my 5th sem, I am getting interested in Engineering as a whole. Subjects like COA, Data Communication were peak. I had a burning desire to learn anything on my way, and this time my rate of learning, ability to grasp concepts quickly overshoot — it felt good.

Now, the 4th attempt, which was mentioned at the starting context:

The day-to-day coding felt monotonous and not mentally stimulating enough — less of my interest, to put it correctly. Don’t get me wrong, there are fields like **cloud, data, system design, LLD** that I find fascinating. But the regular development work wasn’t scratching that itch.

I never understood this research thingy — getting papers published and going on to pursue masters in field of interest under TA/RA-ship. I wanted to read papers and get familiar with this world. PyTorch came into my mind, and it indeed was the go-to tool for modeling and referenced by many AI researchers for their research.

I found a playlist of CampusX on it: Practical **Deep Learning with PyTorch**. And trust me, it was gold. I finally feel much less resistance in learning and implementing stuff like **ANN, CNN, RNN**. It was creating a base for my deep learning understanding. I revisited concepts like activation functions, normal training pipeline (forward pass, loss calculation, back propagation, optimize). I understood the difference between SGD and batch-GD. The thing is, everything is clicking very nicely.

It isn’t that I’ve gained a lot of knowledge — some of my friends are into this field and are learning them from 1st principles, good for them. It’s that I’ve hopped onto too many fields: **design, dev, editing and this**. So, it has become quite clear to me as to what not to do and what to do, to some extent. And trust me, this clarity is very comforting. The **math is mathing**, my *cognitive* and *critical thinking* ability has improved quite a lot.

This was a very vague and not-so-coherent way of me dealing with this to document somewhere. So that was it, thank you :)