Welcome to the Neural Networks Learning Repository! This repository is designed to guide you from the fundamentals of neural networks to advanced topics, complete with practical implementations and real-world projects.
This repository is structured as a series of tutorials, projects, and experiments aimed at:
- Learning from Scratch to Advanced: Start with the basics and progressively explore more sophisticated neural network architectures.
- Demonstrating Library Implementations: Compare implementations using different frameworks (e.g., NumPy for basics, TensorFlow/PyTorch for advanced models).
- Building Real-World Applications: See how neural networks can be applied to solve problems such as sentiment analysis, image captioning, and machine translation.
- 00_basics
- Neural network using NumPy : Implementation of 2-layer basic neural network just using NumPy to recognize hand written digits.
This project is licensed under the MIT License - see the LICENSE file for details.