Well I don't have facebook, snapchat But I do have Insta, kaggle, leetcode, deepML, Medium , Shipd and X.. 🧐🧐Am I getting accepted..?

The best resource for beginners… Coursera lo Andrew NG di Machine Learning specialisation (three courses untaay).. fundamentals anni cover avthaay including labs, assignments. If you want to understand ML mathematically, Prof. Ravindran and Prof. Arun Rajkumar (IIT Madras) playlists untaay YouTube lo. Very useful for deeper understanding. Once you finish this, try to do an ML project. Neeke oka idea ostundi em cheyyali ani because ah course lo chala assignments chesthav real world datasets tho. This is where you have dive deep into Exploratory Data Analysis, Feature engineering, building model, evaluation and improving the model. Algorithms easy ga apply cheyyachu but how do you provide data to a model is very much important. Then after that, Andrew Ng dhe Deep Learning specialisation untundi adi chey. That comprises of 5 courses… excellent untundi. 80% cover aypotundi. Inka deep ga mathematical ga chadavali ante Prof. Mitesh Khapra (IIT Madras) di YouTube lo deep learning playlist untundi. Excellent source. DeepML website lo you can practice coding algorithms. Try to do a new DL project or if there’s a scope, extend your ML project with DL techniques… it depends on your problem statement. Idi one phase of AI/ML… once idi finish aythe tarwatha you can dive deep into LLMs, RAG, Agentic AI.

I have started solving problems on DeepML. What are the other platforms or alternatives?

DeepML, Tensortonic & then Papercode..

Day 62/365 >Solved lc potd >Tried deepml questions, solved 1 Studying for midsems T_T, now for next ~10 days, logs will look similar to this

deepML and tensortonic have been doing it for years now :)

Day recap: > Explored LLM pipelines & orchestration > Tried OpenAI Agents sdk > solved 3 DeepML problems > Brushed up iterators, generators & decorators > Leg day at GYM > Cooked my own Lunch > 127 gm protein

I'll read it too and do some PRs adding more questions to deepML. More people adding questions will make deepML better. But you're yourself a Mixture of Experts 😆(MOE) 🔥

I am starting small with "Matrix-Vector Dot Product" on @real_deep_ml it is embarrassing how long it took me to solve this. I made a mistake at first and thought it was vector * matrix make sure you read the problems well. 😅 deep-ml.com/problems/1 #DeepML #MachineLearning

Already in . Let's see where this goes . Hoping to see deepml do a lot of crazy stuff this year . 🎉🎉👀👀

Investing my time in DeepML feels like investing in Nvidia in 2010. I feel there's a lot of potential in it.

deepml labs 1: MNIST pytorch dataloader pytorch coding patterns i learnt: - per-image statistics (mean/std) & stability - intensity shifts & contrast - clamping/bounding ranges - device & dtype safety

deepml

LeetCode for DSA, HackerRank for SQL, LeetGPU for CUDA, DeepML for ML and many more... all these platforms are great for practice. But day by day, it feels like they’re making the system more rigid and shifting the focus away from the joy of learning.

i did a few, but quite similar to deepml (if not deepml better atm)

this is actually really cool, will be pinning this on my brave sidebar along with deepML 😋😋

Tell me why it is better then deepml?

fr i think contribution on deepml is better as it will make a net better impact

Its very very similar to deepml if am not wrong

Been consistent since Aug 20: > Day 61 of DSA > Day 49 of 1 DeepML Q/day > Did 30 days of Math for ML (paused, restarting soon!) Also built a small project: gaussian-elimination-solver.… Would love X Premium to share my journey & write longer post and reach more learners.