Interested in Artificial Intelligence, Machine Learning, and practical software development.
- Aspiring AI / ML Developer
- Learning Python, Machine Learning, and Artificial Intelligence
- Building educational and practical software projects
- Exploring machine learning algorithms through hands-on implementation
- Using AI coding agents as part of my software development workflow
Programming & Libraries
- Python
- NumPy
- Tkinter
Development Tools
- Git
- GitHub
- AI Coding Agents
From Artificial Neurons to Convolutional Neural Networks and Multiclass Classification.
A collection of 13 interactive educational simulators designed to explore the mathematical foundations and practical mechanics of Machine Learning.
| # | Project | Description |
|---|---|---|
| 01 | One Artificial Neuron | Exploring the mathematical foundations of an artificial neuron. |
| 02 | Perceptron | Binary classification and decision boundaries. |
| 03 | Mini Neural Network | Neural network structure and forward propagation. |
| 04 | Gradient Descent | Optimization and gradient descent mechanics. |
| 05 | Backpropagation | Chain rule and gradient calculations. |
| 06 | Neural Network Training Lab | The neural network training process. |
| 07 | Activation Functions Lab | Activation functions and their derivatives. |
| 08 | Loss Functions Lab | Loss functions and their mathematical properties. |
| 09 | Classification Lab | Classification concepts and model evaluation. |
| 10 | Overfitting & Underfitting Lab | Model complexity and generalization. |
| 11 | Optimizers Lab | SGD, Momentum, AdaGrad, RMSProp, and Adam. |
| 12 | CNN Convolution Lab | Convolution, feature maps, and pooling operations. |
| 13 | Multiclass / Softmax Lab | Multiclass classification and the Softmax function. |
- Learning by implementing algorithms and mathematical models.
- Building interactive applications to visualize complex concepts.
- Using AI coding agents to support code analysis, implementation, testing, and documentation.
- Maintaining project quality through structured development and verification.
Learning by building. Understanding by experimenting.