water
@wateriscoding
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Finally wrote a blog mentioning some of my resources. They are not much but i'll keep updating it (maybe). Also, my first blog so suggestions are welcome.
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be like him.> make an entire os in rust.> live stream the process for 2 hours daily while teaching.> do not care about views and subs. ABSOLUTE CHAD!!!
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Seeing a lot of people read this. Need reviews.
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numpy, pandas, matplotlib.
Postgres, Redis, S3. Name a better trio. I'll wait.
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You know the knowledge is gonna be insane when the ui looks like this.
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This helped me learn everything btw.Most informative videos i found on this topic
converted my neovim to this today.please suggest some more things i can do for design
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Anyone with this vscode theme is my enemy without introduction
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> 90% of my class can't compile code without clicking the small triangle in the top right.> 60% thinks gcc can compile anything.> 30% can't write anything after # include <stdio.h>. That's facts! A little effort and you easily surface above others.
i know 100+ cs grads who don't know how to compile a c hello world.
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wrote my first cuda program today 😅
attending a cuda workshop today.
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this codecrafters is so good man!.we actually need smthn like this for cs degree instead of mugging up those cs textbooks mindlessly
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with this i successfully completed learning ml for 100 days and documented it here. i started from basics and on the 100th day trained by first transformer decoder model. i got to meet a lot of great people here on X. i am excited about the things im working on.
Day 100 of ML:.> Finally completed the training of my gpt (decoder) model. It's a tiny language model trained on the screenplay of Titanic movie with 167k parameters. The results are not too good but they're are fine. > Finally looked a bit into llm fine-tuning and stuff.Cya;)
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Found one more cracked person. Just go for it, you will love his content
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almost completed the karpathy's nn zero to hero series (except for the gpt part, i'll start it now). there was so much to learn, i think i'll come back to it time to time to learn more
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Day 36 of ML:.> almost 10.5 hours into pytorch and learnt about making classification models.> Learnt about 'Sequential'. Had a lot of doubts around how to add activations across each layer but got them.> No cpp today :( .> also stumbled upon a great video by Samson Zhang!. Cya;)
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> gets an unique idea no has built yet.> tell friends to build it together.> sees someone releasing the same idea with better features in the evening .> *sobs again*
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My GitHub is so slop man! i need to build great things. I need to get CRACKED!!!.
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Day 85 of ML:.> Started learning about Transformers (attention) from karpathy lecture, got a lot of info but I'm still confused about shapes and all (i think i need to watch things again), also can you share some beginner friendly resources ;).Cya!)
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never have i ever regretted my low jee score as in the past few months.
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Day 64 of ML:.> added activation functions (sigmoid, relu, tanh) in the go autograd (suggest a good name for this).> created backward function to backpropagate through all nodes (layers, mlp left).> ended the day with studying about constant pointers and pointers with const.Cya;)
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Can someone suggest some good resources for DSA (asking for a friend obv).
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well well well
just built a fully automated Wojak meme generator in Glif in 5 min:. Claude 3.5 block generates the meme as JSON.ComfyUI block uses a Wojak Lora to generate a fitting image.JSON extractor + Canvas Block ties it all together . input "AI entrepreneur" 💀
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Day 65 of ML:.> added a whole neuron func and some other minor functions to the model, also half of layer done .> improved the file structure and created it's packages (i had no idea about packages (skill issue)).> secured an internship.> ended the day doing internship task.Cya;)
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if i don't get this number to atleast 50 by the end of this month, i'll leak my government name, along with my ip address and current coordinates
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Imagine making a model for hours, putting all the hard work, breaking your back, straining your eyes and learning all the concepts you implement you learn along the way, just for it to give you an accuracy of 50%
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Day 33 of ML:.> Decided to learn pytorch in detail and found this video.> learnt everything about tensors and various functions for mat mul and others (can't build stuff due to bad internet + compute issue).> Finally ended the day with regular CPP. Cya ;)
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Day 39 of ML:.> crushed the LA exam today in college.> almost ~15 hours into the pytorch course and got introduced to computer vision.> solved my first kaggle competition in pytorch and got a decent score.Homie got seriously ill so took him to the doc (ended grind early). Cya :)
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Day 31 of ML:.> made a neural network from scratch.> took part in a kaggle competition and submitted the code.> did CPP to end the day. Will improve the competition model tomorrow.I'm at home so I'm slacking off a little. Cya!.
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Day 34 of ML:.> 6 hours into the pytorch course and learnt the fundamentals of tensors.> learnt about loss and optimisers.> Made my first model with class (all the models i made so far were from scratch, as taught in f. ai course), adding train loop tomorrow . Cya;)
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Day 100 of ML:.> Finally completed the training of my gpt (decoder) model. It's a tiny language model trained on the screenplay of Titanic movie with 167k parameters. The results are not too good but they're are fine. > Finally looked a bit into llm fine-tuning and stuff.Cya;)
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Day 37 of ML:.> completed ~14 hours of the pytorch course and learnt about non linear layers and use of their functions.> learnt about multi class classification models and the various activation functions to use (softmax, sigmoid, etc).> Will take part in competition tom.Cya:)
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this is great + every programmer should see the prerequisites of this course (these topics are wonderfully explained!)
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this is such a nice video! explaining the ins and outs of GPU. found it at the best time when im playing around with cuda. too many things to relate with GPU and cuda.
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Day 38 of ML:.> did a lot of linear algebra today, since I have an exam on the same tomorrow (good for ml).> made MNIST model on kaggle for the competition (needs some improvements, tomm).> no pytorch today, but will do it tom.also came across this great vid, check it out. Cya :)
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Day 42 of ML:.> have a computer networks exams tomorrow so I studied for it for almost the entire day.> finally completed ~17.25 hours of pytorch course video.> learnt about creating non linear layers for simple cnn models.Will compensate for the lack of ML tomorrow💪🏻.Cya:)
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if nothing ever works out for me, I just wanna be like him. Just Coding and having fun in my life. High in my own way!!!
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Day 44 of ML:.> Implemented backpropapation from scratch in python, took a lot of time + notes (pen+paper), but it was all worth it(karpathy sensei is a god fr).> will implement the rest of the layer and mlp tomorrow.> was thinking of implementing it in CPP, should i do it?.Cya;)
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Day 88 of ML:.> Mostly caught up with work (working with models, APIs etc.).> Learnt more about RNN (seq2seq, nonseq2seq concepts) .> Also a little info on attention (found this great vid. Do watch).Cya;)
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Day 43 of ML:.> started implementing and making my own cnn model on FashionMNIST dataset. > Tagged along with @Kshitijjkapoor to learn and implement backpropapation from scratch this weekend.> expect a backprop/gradient descent repo in my gh soon;).Cya ;)
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2024 wrapped 🙄.> Started learning ML.> Opened X and met all you guys.> Got my first internship.> Met good folks IRL and got to work with them (still working). - this year was great, i achieved many things and lost a lot of things. I plan on learning and making 2025 even better.
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> i plan to buy a phone.> yt shows all the good videos about the phone.> i order the phone.> yt starts recommending bad reviews of the phone. happens everytime
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Day 53 of ML:.> finally managed to bring the model to train by creating dataloaders of the dataset and then training over them (~72%).> Have to tune the hyperparameters.> learnt about dataloaders, batches, etc. Actually, learnt a lot about these things in the last few days. Cya;)
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Day 63 of ML:.> started making neural networks from scratch in go and made the basic methods and backward methods in it.> I'm aware that i was making makemore mlp and I'll be continuing it (have something in mind).> realised my struggle in pointers and started learning them.Cya;)
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Saw this kid teaching DSA on yt while i haven't even started it yet
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Day 51 of ML:.> Learning about fine-tuning models through the playbook and applying it.> Learning about Transformers and how to train models using int and text data for a project (any resources for them are welcome).Cya;)
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Guys, just started learning about deep learning. When does this Transformers and diffusion come guys!?
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since you guys trashed me for doing matrix multiplication the hard way, here is another way-
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Day 99 of ML:.> Worked some more on improving apis and models .> Implemented multi head and blocks to the transformer but this model won't train due to low gpu 😭, I'll try to train it again on cloud gpu tmrw.(Btw here's the code ->).Cya;)
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New dopamine hit just dropped!.
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added relu, sigmoid and tanh functions in the autograd in go. now going to attempt amazon ml school exam without studying anything. let's see how it goes.
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Day 40 of ML:.> Got ~16 hours into the pytorch course and now learning computer vision basics.> learnt about dataloaders, flatten layer and other layers in the model. > couldn't do much since exams are ongoing and had to study for tomorrow, will compensate it tomorrow ;). Cya :)
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i love my feed when everyone is posting their W's, posting code snippets and trying out new things. honestly, it inspires me to do more things.
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Day 86 of ML:.> Worked a lot on api and finally solved a major bug bugging me (now it shouldn't take much long to finish) .> Also continued learning about Transformers, learnt a little about RNNs (FAFO) and found a great lecture around it, will continue more of it.Cya;)
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The main problem with new programmers like me is learning higher level programming and completely ignoring the underlying processes that run in the background and run the entire thing.
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One of the best ways to learn CS is to build projects around the subjects in the sem.I learned it a little later.> Build projects.> Learn concepts.> Score good marks.
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Day 41 of ML:.> Watched cs231n's backprop lecture to understand backprop basics in depth, i might make it from scratch.> ~16.5 hours into the pytorch course and learnt to make dataloaders, training and testing loops functions and other things.> will revise cnn before sleep. Cya:)
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> friend goes for an internship interview.> Has "R" language in his resume.> Interviewer confused, asks other interviewer of R means "react language".> Friend runs for life
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Day 35 of ML:.> ~8.5 hours into the course and i completed the second part, learnt about losses, optimisers and other stuff.> tried building my own model on my laptop, had to cut down 1.1 million rows down to 10k rows (CPU issue).> Finally some CPP to end the day. Cya ;)
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ML update: .> Guys due to a very tragic unfortunate event I wasn't able to study ml today and probably won't do it for a few more days.
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Can't understand the uni professor's hate for LLMs. Not allowing to use it while it is the best tool to learn things.
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Day 46 of ML:.> Participated in the kaggle competition and made the model myself, used the wrong optimizer 🤦🏻 but then changed it and the results improved (took a lot of time), will improve it more tomorrow.> also completed the implementation part of neuron, layers and mlp.Cya;)
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Final year project team members selection is where the friendships break.
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I think no one has had a greater influence over the ml community as karpathy sensei. I mean it's because of him that people are able to think about making LLMs not just in python but in other languages. I don't know if I'll be able to meet him in this life but i owe him a lot.
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Learnt a little about computer vision and CNNs and my mind is already full of ideas.
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Won this hackathon .LFG 🚀 🚀.
Passed the coding round. Into the final round now.
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Day 71 of ML:.> completed and implemented the mlp part of wordmore in python (enjoyed it, left with go but guys build multidimensional things first).> started with activations, gradients and batch norm part of wordmore (the rnn architecture).Will complete it soon too!!!.Cya;)
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Guys joining reddit AGAIN. Please suggest some good subreddits for ai/ml/dl stuff obviously.
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Day 45 of ML:.> completed the neuron, layers and mlp part of the nn video, i will implement it in code tomorrow.> Got the TOC exam tomorrow so spent most of the day studying for it.> Will do more tomorrow.Cya;)
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Day 75 of ML:.> Rebuilt the entire transformer model i made a few days ago to find shortcomings and improve it (took the entire day but it was worth it + i understood most of the code this time).> advanced on my way to become a backprop ninja (this is really helping me out).Cya;)
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Bro calls himself a programmer but couldn't install the compiler on the lab pc 😭.
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Day 32 of ML:.> spent half of the day figuring about hidden layers and neurons until i made it from scratch. > added the layer to the model but the laptop memory ran out .> Eureka moment when I saw how to make nn with classes in pytorch.> Finally some CPP to end the day. Cya ;).
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Guys after completing fastai part 1, i don't think I'm still ready for part 2 (the diffusion thing looks tough), what would you suggest I do now?.
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I f****ing hate my college fr. Lost another previous opportunity thanks to them.
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