This repository demonstrates the foundational principles of developing a linear regression model entirely from scratch. The project encompasses the construction and visualization of a linear regression algorithm, including the essential components of feed-forward computation and backpropagation for model training. The approach is hands-on, focusing on understanding the underlying mechanics of linear models and gradient-based optimization. This project draws inspiration and partial adaptation from Andrej Karpathy's micrograd project.

• Value Class: The Value class is central to the framework, encapsulating a scalar value and its gradient. It's vital for constructing the autograd graph, enabling efficient execution of forward operations and the backward propagation of gradients

• Hypothesis Function: Articulates the linear regression formula (y = xw + b), with (w) and (b) as parameters, to predict outcomes. It defines the model's structure for making predictions.

• Graph Visualization: Leverages graphviz to depict the model's computational graph, aiding in comprehending data and gradient flows for easier debugging and learning.

  

• Optimization Loop: Illustrates model training via gradient descent, iteratively refining parameters to reduce loss, demonstrating the adjustments for error minimization.

  

• Loss Function: Adopts Mean Squared Error (MSE) to evaluate model precision, measuring the discrepancy between predictions and actual targets, serving as the optimization criterion.

  

To explore the linear regression model, clone this repository and run the demo.ipynb notebook (or open in Google Colab ). The notebook is structured to guide you through each step of the model development process, from generating synthetic data to training the model and visualizing its performance.

This project offers a deep dive into the mechanics of linear regression, providing a solid foundation for understanding machine learning algorithms' basic principles. By building the model from scratch, it encourages a hands-on approach to learning, demystifying the complexities behind machine learning models.