This repository serves as a portfolio of advanced research and practical implementations in the field of Deep Learning. It explores the mathematical foundations of artificial neural networks and extends to the design, training, and optimization of complex architectures using modern frameworks.
The repository is structured around key paradigms in deep learning, demonstrating both low-level mathematical understanding and high-level framework proficiency:
- Algorithmic Foundations:
- Backpropagation: Implementation and analysis of the backpropagation algorithm, gradient descent optimization, and chain-rule calculus essential for weight updates.
- Advanced Architectures:
- Convolutional Neural Networks (CNNs): Spatial data processing and feature extraction for image-based tasks.
- Recurrent Neural Networks (RNNs): Sequential data modeling and temporal dependency tracking.
- Autoencoders: Unsupervised learning models for dimensionality reduction, feature compression, and latent space exploration.
- Optimization & Generalization:
- Regularization Techniques: Extensive experiments with L1/L2 regularization, Dropout layers, and early stopping to mitigate model overfitting and improve generalization on unseen data.
- Frameworks:
PyTorch,TensorFlow - Data Processing:
NumPy,pandas - Focus Areas: Gradient descent optimization, loss function minimization, hyperparameter tuning, and architectural scaling.
This project showcases the ability to bridge the gap between theoretical deep learning concepts and applied AI engineering. It highlights a structured approach to solving complex non-linear problems while ensuring computational efficiency and model robustness.