This repository showcases a collection of deep learning projects focusing on neural network architectures, implementation from scratch, and comparative analysis using popular frameworks like TensorFlow and PyTorch. The projects emphasize understanding and applying recurrent and feedforward neural networks to various non-NLP tasks such as time-series prediction and general data modeling.
This portfolio of projects serves as a practical demonstration of deep learning techniques and neural network design principles beyond natural language processing. Through hands-on experiments, the repository explores foundational models like RNN, LSTM, GRU, and basic feedforward networks, implemented in Python with full explanations.
The repository aims to impart a clear understanding of:
How different recurrent neural network variants handle temporal data
Building neural networks from first principles vs. using advanced ML frameworks
Comparative performance evaluation and model selection criteria
Extending architectures to more complex hybrid or convolutional schemes
RNN vs LSTM vs GRU Comparison: A detailed comparison of three popular recurrent models focused on sequence learning capabilities and performance benchmarks.
Feedforward Neural Network from Scratch: An implementation of a classic neural network built entirely with Python and NumPy for foundational understanding.
CNN and Hybrid Model Explorations: Early experiments with convolutional and hybrid architectures, expanding modeling power for complex data patterns and image-related tasks.
##Technologies and Tools Used
Programming Language: Python 3.10+
Development Environment: Jupyter Notebook
This repository will continually grow with implementations of advanced networks and benchmarks, including but not limited to:
Transformer-based architectures adapted for non-NLP tasks
Autoencoders and variational models
Deeper convolutional networks for image and signal processing
Extensive model tuning and performance optimization pipelines
Deep Learning Frameworks: TensorFlow 2.x, PyTorch 2.x
Data Manipulation & Visualization: NumPy, Pandas, Matplotlib, Seaborn