About ML Visualizer
An interactive educational platform to help you understand machine learning concepts through intuitive visual demonstrations.
Project Overview
ML Visualizer is designed to bridge the gap between theoretical knowledge and practical understanding of machine learning concepts. Whether you're a student, educator, or professional, our interactive visualizations make complex algorithms more approachable.
The application features modular visualizations that allow you to manipulate parameters in real-time and observe how different algorithms behave under various conditions, providing valuable insights into the inner workings of machine learning models.
Educational Benefits
- •Visualize abstract mathematical concepts in intuitive ways
- •Experiment with parameters to develop algorithmic intuition
- •Understand complex behaviors through interactive animations
- •Bridge theory and practice with hands-on learning
Linear Regression
Explore how linear regression finds the best-fitting line for a set of data points. Visualize the gradient descent algorithm in action as it iteratively improves the model parameters.
- Adjust learning rate and observe convergence speed
- Manipulate slope and intercept manually
- Generate random datasets to test the algorithm
Decision Boundaries
Understand how classification algorithms separate data into different classes by creating decision boundaries. Compare multiple algorithms on the same dataset.
- Create your own dataset by clicking on the visualization
- Switch between Logistic Regression, SVM, and KNN algorithms
- Adjust algorithm parameters and see how boundaries change
Neural Networks
Visualize the architecture of neural networks and see how signals propagate through the layers. Understand activation functions and their effects on the network's behavior.
- Customize network architecture (layers and nodes)
- Experiment with different activation functions
- Watch animated signal propagation through the network
Convolutional Neural Networks
Explore how Convolutional Neural Networks (CNNs) process images for digit recognition. Visualize each step of the CNN pipeline from input image to classification output.
- See convolutional filters in action detecting edges and patterns
- Visualize pooling operations and feature extraction
- Watch the step-by-step processing of digit recognition
The visualizations are designed to be educational rather than production-ready ML tools. They simplify complex concepts to make them more accessible while maintaining mathematical accuracy.