avinash singh @_Avinashk
Trying to connect the dots between curiosity and understanding. Joined February 2025-
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Don’t chase intensity early, chase repetition. Neural circuits strengthen through frequency, not force. Show up daily, even at 20% capacity, and the loop solidifies. Momentum is the compound interest of consistency; small deposits over prolonged procrastination.
I have always been fit... now can’t wait to get jacked
I have always been fit... now can’t wait to get jacked https://t.co/XjZ8j7NPtv
Don’t aim for flawless. Aim for finished.
Update (15th Aug – 19th Aug): busy with college work these days but still trying to stay consistent -started learning flask & sql -practiced 10 leetcode problems in the last 4 days -explored and did some practice on kaggle
my last 3 day update: - finished chapter 2 of Hands On machine learning - completed worldquant applied data science lab – housing in mexico lab - explored kaggle - solved 6 leetcode problems
How do you build domain knowledge that actually helps in ml projects ? need tips
kaggle is so time consuming man. I open it start working on data and 3-4 hours just fly by without me even realizing. But it's so much fun , I am really enjoying it.
The more I read and think about philosophy the less I believe in the idea of a religious god. for me god is just the universe itself the one that created everything. Prayers and rituals just don't feel real to me.
got through part of Chapter 2 of hands On machine learning over the last two days -covered: big picture, framing the problem, performance measures, checking assumptions, getting the data, quick look at data and creating a test set -Solved 4 problems on leetcode
Football is the best thing ever invented in human history Iylyk
-Finished Chapter 1 of Hands-On Machine Learning over the last two days -Did some recursion practice questions -Learned about hashing -Solved 2 LeetCode problems -Spent some time on Kaggle
Whoever programmed us? Man’s an unimaginable genius programmer.
-Revise probability and solve some questions -Study space complexity and recursion in dsa -Learn multiclass classification in ml and evaluation metrics
-revised binary classification in ML -did some logic building questions -explored Kaggle and did EDA + analysis on NBA data
Okay
Okay https://t.co/1aLMTsKo79
-Revised algorithm concepts in ml -Started reading hands-On ml with scikit-learn -Started DSA from scratch -Learned about time complexity and understood the proof of big O will go slow with dsa, not rushing anything. remind: don’t let peer pressure hit and rush things
getting back to my ml journey after a long break. mind was too distracted last few weeks, could not focus on anything properly. things feel lighter now so slowly picking it up again. also thinking to start dsa . if anyone got a good roadmap or tips, would mean a lot.
China showed the world how self belief can turn into power. India has even more people, even more potential. what's stopping us from doing the same?
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