DEEP LEARNING

Overview

This repository is dedicated to deep learning concepts, covering activation functions, optimization techniques, and general deep learning architectures for various datasets. It serves as a great starting point for understanding and implementing deep learning models.

Checklist

• Gradient Descent
• Loss
• Activation Functions
• Multi-layer Perceptron
• Convolutional Neural Network
• Recurrent Neural Network
• Long-Short Term Memory
• Autoencoder

Getting Started

Prerequisites

To run the notebooks, ensure you have the following installed:

• Python (>=3.7)
• Jupyter Notebook
• TensorFlow / PyTorch
• NumPy, Pandas, Matplotlib

Installation Guide

Fork the Repository

• Click the Fork button (top-right corner).
• This creates a copy of the repository under your GitHub account.

Clone Your Forked Repository

git clone https://github.com/your-username/deepLearning.git
cd deepLearning

Replace your-username with your actual GitHub username.

Create a New Branch (For Your Changes)

git checkout -b feature-branch

Replace feature-branch with a meaningful branch name.

Make Changes and Commit

Modify the code, then stage and commit:

git add .
git commit -m "Description of changes"

Push Changes to Your Forked Repository

git push origin feature-branch

Create a Pull Request (PR)

1. Go to your forked repository.
2. Click on Compare & pull request.
3. Ensure the base repository is the original repo and the head repository is your fork.
4. Add a meaningful title and description.
5. Click Create pull request.

Key Topics Covered

Topic	Description
Activation Functions	Understanding different activation functions used in neural networks.
Gradient Descent Variants	Exploring different types of gradient descent algorithms.
Multi-Layer Perceptron (MLP) Models	Studying MLP architecture and its applications.
Image Classification using CNN	Implementing CNNs for image classification tasks.
Loss Functions	Understanding various loss functions used in deep learning.
Recurrent Neural Network (RNN)	Sequential data processing excels in tasks like text analysis and language modeling..
Long Short-Term Memory (LSTM)	An advanced RNN variant designed to handle long-range dependencies in sequences.

Future Improvements

• Implementing probabilistic models.
• Explore additional optimization techniques.
• Add more advanced deep learning models.

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

Code of Conduct

Please read our Code of Conduct before contributing to this project.

Security

If you discover a vulnerability, please refer to our Security Policy for instructions on how to report it responsibly.

License

This project is licensed under the MIT License.

Author

Aakaash M S