Uploaded June 2021 | Updated September 2026, 2 weeks ago
The purpose of this project which is placed in Github is to understand and compare how different Machine Learning (ML) models works with different set of hyperparameters.
For example, run a Python script dedicated to KMeans or Gradient Descent, pass you arguments and see how training progress or training data is distributed in both 2D and 3D spaces. On the top of that, project provides additional projections data for several of available ML models.
In the current version of this project there are following models available for visualizations:
- Gradient Descent (2D/3D)
- KMeans (2D/3D)
- KNN (2D/3D)
- Linear regression
- Linear regression (for non-linear scenario)
- Logistic regression
- Logistic regression (for non-linear scenario)
In this video I went through examples of Gradient Descent and KMeans. Here I introduce how to create a Python virtual environent for this project, clone the Github repository (links is below) and execute files.
Link to the Github: github.com/Gautam-J/Machine-Learning
As stated in Github description:
This project was initially started to help understand the math and intuition behind different ML algorithms, and why they work or don't work, for a given dataset. The author started it with just implementing different versions of gradient descent for Linear Regression. The author also wanted to visualize the training process, to get a better intuition of what exactly happens during the training process. Over the course of time, more algorithms and visualizations have been added.
So, again - this cool Github repo allows you to look inside selected ML algorithm and experiment with you hyperparameters to better understand how these algorithms really works!
Enjoy and have fun with this!
#machinelearningalgorithms #gradientdescent #linearregression
The purpose of this project which is placed in Github is to understand and compare how different Machine Learning (ML) models works with different set of hyperparameters.
For example, run a Python script dedicated to KMeans or Gradient Descent, pass you arguments and see how training progress or training data is distributed in both 2D and 3D spaces. On the top of that, project provides additional projections data for several of available ML models.
In the current version of this project there are following models available for visualizations:
- Gradient Descent (2D/3D)
- KMeans (2D/3D)
- KNN (2D/3D)
- Linear regression
- Linear regression (for non-linear scenario)
- Logistic regression
- Logistic regression (for non-linear scenario)
In this video I went through examples of Gradient Descent and KMeans. Here I introduce how to create a Python virtual environent for this project, clone the Github repository (links is below) and execute files.
Link to the Github: github.com/Gautam-J/Machine-Learning
As stated in Github description:
This project was initially started to help understand the math and intuition behind different ML algorithms, and why they work or don't work, for a given dataset. The author started it with just implementing different versions of gradient descent for Linear Regression. The author also wanted to visualize the training process, to get a better intuition of what exactly happens during the training process. Over the course of time, more algorithms and visualizations have been added.
So, again - this cool Github repo allows you to look inside selected ML algorithm and experiment with you hyperparameters to better understand how these algorithms really works!
Enjoy and have fun with this!
#machinelearningalgorithms #gradientdescent #linearregression
