This repository documents my structured journey in deep learning, covering core concepts through hands-on experiments, visualizations, and analysis.
To build a strong foundation in deep learning by implementing and analyzing key concepts step by step.
Each study includes:
- 📓 Notebook (implementation)
- 📊 Visuals (graphs and results)
- 📄 README (concept explanation + observations)
- Study01 - ANN (preprocessing/image resizing)
- Study02 - Single layer percentron & ANN
- Study03 - MLP & Backpropagation(Basic XOR)
- Study04 - Image Classification (using MLP)
- Study05 - Gradient Descent Optimizer (Applying: image Dataset)
- Study06 – Heuristics in Backpropagation
- Study07 - CNN-Basics
- Neural Networks
- Single layer neural network
- Forward propagation
- Backpropagation
- Image Classification
- Optimization Techniques
- Learning Rate Scheduling
- Heuristics in Training
- Python
- PyTorch
- NumPy
- Matplotlib
To build a complete, well-documented deep learning repository from basics to advanced architectures.
- Clean and modular code
- Experiment-based learning
- Visual comparison of models
- Real understanding of training behavior
Osam Sami