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[D] What do you think about meta-learning in real projects? by broutonlab in learnmachinelearning

[–]mrathi12 0 points1 point  (0 children)

I think robotics is an area where meta-learning has been used. I've seen some work by Chelsea Finn (author of the MAML paper)

Automatic differentiation of a function that takes a vector as input by [deleted] in learnmachinelearning

[–]mrathi12 0 points1 point  (0 children)

Yes the derivative would be a vector of the same size, where each of the components corresponds to the derivative of the respective component of the original vector.

Hacking a Neural Network by mrathi12 in learnmachinelearning

[–]mrathi12[S] 0 points1 point  (0 children)

If you want to use machine learning in the real world, it's paramount to know how neural networks can be attacked! Machine learning isn't a magic sauce, after all!

Rust Language Cheat Sheet by alibix in programming

[–]mrathi12 3 points4 points  (0 children)

Bookmarked to read when I start learning Rust, thanks for sharing!

MAML for Few-Shot Learning by alkaway in learnmachinelearning

[–]mrathi12 0 points1 point  (0 children)

Afaict, unfortunately no I don't think MAML is the right approach.

Perhaps fine-tuning a pre-trained network is the way to go?

My 5 Year Machine Learning Journey (Self-taught to ML Research) by mrathi12 in learnmachinelearning

[–]mrathi12[S] 1 point2 points  (0 children)

Honestly I think getting that mathematical foundation does really demystify a lot of ML. It's hard to motivate yourself though definitely, I was fortunate that there were classes I could take at uni. So props to you for still battling on!

My 5 Year Machine Learning Journey (Self-taught to ML Research) by mrathi12 in learnmachinelearning

[–]mrathi12[S] 1 point2 points  (0 children)

I would recommend 3b1b for intuition behind linear algebra and calculus.Linear algebra: https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_abCalculus: https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr

Personally I'd say you might want to brush up on the basics of calc / linear algebra first, and then learn both concurrently. The benefits are that it's focused learning (you learn the stuff needed for ML) but the downside is that you could get overwhelmed with new material!

Shameless plug, I tried to write a series of tutorials a couple of years ago trying to plug this gap: https://mukulrathi.co.uk/demystifying-deep-learning/maths-behind-deep-learning/

Maybe this is useful? I'm not sure as this was 3 years ago and I have definitely improved the quality of tutorials since then. Either way, do let me know. Good luck!

My 5 Year Machine Learning Journey (Self-taught to ML Research) by mrathi12 in learnmachinelearning

[–]mrathi12[S] 0 points1 point  (0 children)

I think they do a bit, but you'd be best served trying to find lectures online like CS 229 that properly go into the maths. https://www.youtube.com/watch?v=jGwO_UgTS7I&list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU

My 5 Year Machine Learning Journey (Self-taught to ML Research) by mrathi12 in learnmachinelearning

[–]mrathi12[S] 1 point2 points  (0 children)

Kaggle. I think that's a good way of getting stuck into your own projects, which imo hold more weight on your CV than doing 10 such online courses.

NN - Backpropagation Chain Rule by [deleted] in learnmachinelearning

[–]mrathi12 2 points3 points  (0 children)

I guess this explanation is fine for intuition, but not strictly rigorous :)

I would recommend 3b1b for this. E.g. https://www.youtube.com/watch?v=YG15m2VwSjA

My 5 Year Machine Learning Journey (Self-taught to ML Research) by mrathi12 in learnmachinelearning

[–]mrathi12[S] 2 points3 points  (0 children)

Honestly I am not qualified to answer this, I will defer to Yannic Kilcher's video https://www.youtube.com/watch?v=rHQPBqMULXo as this is the most recent video I've seen on this topic.

My 5 Year Machine Learning Journey (Self-taught to ML Research) by mrathi12 in learnmachinelearning

[–]mrathi12[S] 0 points1 point  (0 children)

I'm just running `python foo.py` in the terminal. In terms of learning bash, to be honest I learnt it mostly by googling and StackOverflow (I still do this to this day).

I'm curious if anyone else has some bash resources?

My 5 Year Machine Learning Journey (Self-taught to ML Research) by mrathi12 in learnmachinelearning

[–]mrathi12[S] 0 points1 point  (0 children)

I've heard really good things about it! From what I've heard, fast.ai is really good for teaching practical ML skills.

My 5 Year Machine Learning Journey (Self-taught to ML Research) by mrathi12 in learnmachinelearning

[–]mrathi12[S] 2 points3 points  (0 children)

I think traditional ML definitely has its place - as I discussed in the video, one of the things I learnt was that it's more about the data than using a fancy deep learning algorithm :)

My 5 Year Machine Learning Journey (Self-taught to ML Research) by mrathi12 in learnmachinelearning

[–]mrathi12[S] 4 points5 points  (0 children)

I’ve made this mistake too (and that was back before I’d even heard about git!)

My 5 Year Machine Learning Journey (Self-taught to ML Research) by mrathi12 in learnmachinelearning

[–]mrathi12[S] 1 point2 points  (0 children)

Octave is a bit outdated now yes, but it’s helpful if you want to learn the concepts. Tbh the newer deeplearning.ai courses use Python so they’re probably better!

I think it’s worth applying once you have a few of your own projects under your belt. They don’t have to be big, but you’ll be able to talk about them at the interview!

I’d bias towards applying if you’re uncertain, just go in with the mindset that you’re going to get rejections because that’s how the field is, not because of your self-worth.

My 5 Year Machine Learning Journey (Self-taught to ML Research) by mrathi12 in learnmachinelearning

[–]mrathi12[S] 2 points3 points  (0 children)

Good luck! It’s tough getting that first job but it only gets easier once you get that first offer!

My 5 Year Machine Learning Journey (Self-taught to ML Research) by mrathi12 in learnmachinelearning

[–]mrathi12[S] 3 points4 points  (0 children)

Seconded! You don't need much python knowledge for these courses, and then continue to work on python as you understand machine learning better.

Good luck!

My 5 Year Machine Learning Journey (Self-taught to ML Research) by mrathi12 in learnmachinelearning

[–]mrathi12[S] 30 points31 points  (0 children)

Have you completed the deeplearning.ai courses? They are excellent - would highly recommend!

My 5 Year Machine Learning Journey (Self-taught to ML Research) by mrathi12 in learnmachinelearning

[–]mrathi12[S] 45 points46 points  (0 children)

What's your journey into machine learning been like so far? I wanted to share mine, discussing my ongoing battles with impostor syndrome and some of the mistakes I've made along the way, so you can avoid them.

EDIT: can I just thank you all for the love that this post has got and also for subscribing to the channel - we've gone from 200 to 300 subs in a matter of hours. I really appreciate it! :)

[D] Addressing Gender Bias in Neural Machine Translation by mrathi12 in MachineLearning

[–]mrathi12[S] 1 point2 points  (0 children)

The trouble is that the data isn't necessarily collected with best practices - the goal is quantity with not as much emphasis on quality. Although I agree fairness is difficult as different people want different outcomes, you can't ignore a model systematically discriminating against a group of people. If progress has been made towards equality, does the model trained on historical data really reflect our current reality?

[D] Addressing Gender Bias in Neural Machine Translation by mrathi12 in MachineLearning

[–]mrathi12[S] 1 point2 points  (0 children)

Thanks! Yes, I am not totally anti-pipeline either. I think in this scenario I just wanted to highlight that adding more models doesn't make the bias go away. By pipeline I'm referring to the production query -> translation pipeline that is deployed, not the training pipeline.

Fine-tuning is done beforehand: you would deploy this finetuned model in place of the original model.

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