A collection of small, beginner-friendly deep learning projects to help anyone start their journey into AI and neural networks. Each project is thoroughly documented and written with clarity to make complex concepts accessible.
This repository serves as both my personal learning journey and a resource for others starting out in deep learning. Each project focuses on a specific concept or technique, implemented in a way that prioritizes understandability over optimization.
Each project folder contains:
- Well-commented Python code (Most the time in Jupyter Notebook)
- Step-by-step explanations of concepts
- Visualizations where helpful
- Requirements and setup instructions
- Suggested modifications to extend your learning
- Clone this repository
- Install the required dependencies (see individual project Requirements)
- Explore the projects in any order - each is designed to be self-contained
- Experiment by modifying the code and observing the results
- Basic Python knowledge
- Familiarity with NumPy is helpful ( other libraries will be used on some projects )
- Curiosity and patience!
- Project 1 ---> MNIST Digit Classifier Using NN
- Project 2 ---> MNIST Digit Classifier Using CNN
(more comming soon)
Feel free to fork this repository, experiment with the code, and submit pull requests if you've made improvements or fixed bugs. The goal is collaborative learning!
Here are some resources that have helped me on my deep learning journey:
If you find this repository helpful, please consider giving it a star. It helps others discover these learning resources!
This project is licensed under the MIT License - see the LICENSE file for details.
Happy Learning! 🎉