6 APIs for your next project
1. Fine-tune LLMs without writing a single line of code
https://t.co/57z1Qv0ewE
2. Pokemon API
https://t.co/Cw6c4jl28D
3. Memes API
https://t.co/9ipY0JvVzh
4. Dogs API
https://t.co/igWfu0Hwgf
5. Bored API
https://t.co/oLZt1vUaZZ
PCA from scratch using Python 🔥
Principal Component Analysis is a powerful technique for dimensionality reduction and data visualisation.
-- step by step explanation with code --
Let's go! 🚀
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Generative AI can change the way you work:
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Text to website
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Fully Funded Scholarship in Switzerland ($27,000/Year)
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Linear Regression clearly Explained!🚀
Linear Regression models relationship between a dependant variable (y) & two or more independent variables (x1, x2 ...)
❗️For the sake of simplicity we discuss linear regression with a single independent variable.
Mathematical Representation:
y=mx+c
where:
- y is the dependent variable
- x is the independent variable
- m is the slope of the line
- c is the y intercept
Cost Function:
❗️The goal is to find the best values for m and c that minimize the error between the predicted values & the actual values for all data points.
This is how the cost function looks, a mean squared error:
MSE= 1/N∑(yi−(m*xi+c))**2
Where:
- N is the number of data points.
- yi is the actual value for the ith data point.
- mxi+c is the predicted value for the ith data point.
How it works (Optimisation)❓
To minimize the MSE, we can use gradient descent.
The basic idea is to:
- Start with some initial value of m & c
- Compute the gradient of the MSE w.r.t. both m & c.
- Update m & c in the direction of the negative gradient.
Implementation from Scratch:
Now that we understand how things work it's fairly simple to implement linear regression in Python!
Check this out👇
That's a wrap, If you interested in:
- Python 🐍
- Machine Learning 🤖
- Maths for ML 🧮
- MLOps 🛠
- CV/NLP 🗣
- LLMs 🧠
Find me → @akshay_pachaar ✔️
Everyday, I share tutorials on above topics!
Cheers!🥂