u/LostAd4986

I've never really been a reader. Books usually lost me a few chapters in.
My first attempt at learning machine learning was the usual route — one YouTube playlist after another. It felt like watching something, not learning it. Nothing really stuck.
So I picked up Hands-On Machine Learning by Aurélien Géron. And somehow, I ended up reading a 1000+ page book . Every chapter, I ran the code myself, broke it on purpose, and debugged it until I understood why it worked — alongside college lectures, assignments, and exams.
Somewhere along the way, something shifted in how I learn.

I stopped reaching for the fastest explanation and started reaching for the actual source — documentation, research papers, and technical writing I would've previously skipped for a quicker video.
In the middle of learning the ML pipeline basics, I built a GoogLeNet-style CNN with a custom DepthPool layer, and many more things at low level.

That's when it stopped feeling like an exercise and started feeling like something I could actually own — chasing shape mismatches, tracing silent preprocessing bugs, and retraining models more times than I'd like to admit.
From there, I kept rebuilding things: RNNs, attention mechanisms, transformers, autoencoders, GANs, diffusion models, RL. Each one broke in a different way, and each one taught me something different when I had to figure out why.
I'm still going deeper into Computer Vision and NLP from here. Those are the areas I keep getting pulled toward.
I still think YouTube has its place.
But this book is what made me a reader in the first place — and now research papers and documentation are where I actually go to learn.
Still early in this. Still building. Just glad I stuck with it.
hashtag#MachineLearning hashtag#DeepLearning hashtag#ComputerVision hashtag#TensorFlow hashtag#Keras hashtag#LearningInPublic

Gujarati has ~55M speakers and almost no open NMT tooling compared to Hindi/English, so I built a from-scratch Transformer encoder-decoder (18M params, 2 blocks, 8 heads) trained on the English-Gujarati Machine Translation dataset, aiming to get this working end-to-end inside a single 10-hour/16GB GPU session.

A few things I had to work through that might be useful to others hitting the same walls:

Fixed a memory blowup from computing softmax + sparse_categorical_crossentropy separately — switching to raw logits + from_logits=True avoided materializing a full (batch, seq_len, vocab) float32 tensor twice. Added wall-clock-based checkpointing (not just epoch-end) after losing a run to a mid-epoch session cutoff. Word-level vocab (32k tokens) is giving me an OOV rate of [20]% on Gujarati — planning to try subword tokenization next unless there's a better lever I'm missing.

Notebook's here if you want to see the full training setup: [ https://www.kaggle.com/code/neelshah58/eng-guj-translation-using-18m-parameter-model ]. Genuinely open to "you're overcomplicating this" as an answer too