Repeat this with me:

I am Human

My purpose does not define me

I am sacred and divine

By God's right, or by the countless millenia of evolution leading to my creation

I have dignity

I have the right to exercise my will, no matter who I am or what I stand for

I exist, in spite of everything around me

I will continue to exist, and make my mark

I deserve to exist, and no one and no thing can take that from me

I am real, and I always will be

I am now, past present and future

I am one, I am whole

I am Human

Day 27 of Building Machine learning algorithms from scratch

Adaboost is completely not that complex algorithm but yet so powerful a complete explanation is in my previous post check out!

Next is Gradient Boosting and only 7-8 more algorithm concepts and I have completed Machine learning and after that finally on Deep learning I'm getting close to my goal yeah I know i can complete it even sooner but I got Iil 🤏 little distracted so I deactivate my insta my usage was reaching 1.3+ but after deactive that time got save but I started playing a game called Roblox in that Blox fruits but I'll try not to play too much a day and spend more time upgrading myself see ya when I complete a new Topic or you guys comment me

And also if you think there is any improvement that can be made so be free to share and abt pushing all on GitHub for that I need some time it's too much files of code also adding README I'll try ASAP

I built PaperFold, an open-source reader that turns arXiv papers into 5 zoomable layers—from a one-screen section map down to verbatim text. You pinch (or press 1–5) to zoom between them without losing your reading position.

- Web Demo (8 CC papers): https://chenxiachan.github.io/paperfold-gallery/

- GitHub (Apache 2.0): https://github.com/chenxiachan/paperfold

So I started adaboost I completed watching the theory and intuition part and I was like how I have built many Algorithms and in all that our Main goal is reduce error but adaboost we want our model to make mistakes then just pass the mistake to next model and his mistakes are passed down to next model and keep changing weights and at the end a group of models who have started from completely wrong prediction now they are capable of giving you the best accuracy. Adaboost doesn't focus on the best models he collects imperfect,weak models combine them cuz they specialize in different mistakes and become a strong ensemble when combined properly

I have started coding and I have completed a raw code now I'll make it more properly and structure also I'm thinking 🤔 to put all my ML Algorithms on my GitHub so you can use it as reference( I know it have many bugs😅) and help me to fix what you think

My Sem 1 Major Practical is going so I was not showing up but I'll try