๐ ๐ง๐ฟ๐ฎ๐ถ๐ป๐ฒ๐ฑ ๐ ๐ ๐ฎ๐ฐ๐ต๐ถ๐ป๐ฒ-๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด ๐ ๐ผ๐ฑ๐ฒ๐น ๐ถ๐ป ๐ฐ๐ฌ ๐๐ถ๐ป๐ฒ๐
Most people import a library and call it a day. They use pre-built tools to do the work. You should know how those tools work.
I built a linear regression model from scratch. No black-box libraries used. This is Day 1 of building every core algorithm by hand.
A model is a simple equation: y = mx + b. Training means finding the best values for m and b to fit your data.
Here is the process:
The Prediction You guess a value using your current m and b.
The Error You calculate the Mean Squared Error (MSE). You subtract the prediction from the actual value and square the result. This makes large errors count more.
The Gradient You use calculus to find the direction of the error. You want to move in the direction that makes the error smaller.
The Step You update m and b using a learning rate. This rate determines your step size.
- Small learning rate: Training takes too long.
- Large learning rate: The model overshoots and fails.
This loopโmodel, loss, gradient, stepโis the engine for neural networks and deep learning. Once you master this loop, you understand the foundation of modern AI.
Watch the model learn live here: https://dev48v.infy.uk/ml/day1-linear-regression.html
Read the full breakdown here: https://dev.to/dev48v/i-trained-a-machine-learning-model-from-scratch-in-40-lines-no-scikit-learn-2n29