Upload README.md

README.md

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----
-title: MLP Visualizer
-emoji: 🚀
-colorFrom: red
-colorTo: red
-sdk: docker
-app_port: 8501
-tags:
-- streamlit
-pinned: false
-short_description: Deep Learning concepts
-license: mit
----
-
-# Welcome to Streamlit!
-
-Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
-
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+---
+title: Interactive MLP Learning Platform
+emoji: 🧠
+colorFrom: blue
+colorTo: indigo
+sdk: streamlit
+sdk_version: 1.32.0
+app_file: app.py
+pinned: false
+---
+
+# Interactive MLP Learning Platform
+
+This is an interactive web application designed to help students learn about Multi-Layer Perceptrons (MLPs) and deep learning concepts. The application allows users to:
+
+1. Generate synthetic datasets with customizable features and classes
+2. Split data into training, validation, and test sets
+3. Design and visualize MLP architectures (including per-layer activation functions)
+4. Train MLPs and observe the learning process with real-time training and validation metrics
+5. Visualize the results and model performance, including:
+    - Training/validation loss and accuracy curves
+    - Weight and bias visualization
+    - Weight optimization over epochs
+    - Network architecture diagram
+    - Confusion matrix and classification metrics after testing
+
+## Setup Instructions
+
+1. Install the required dependencies:
+```bash
+pip install -r requirements.txt
+```
+
+2. Run the Streamlit application:
+```bash
+streamlit run app.py
+```
+
+## Features
+
+- Interactive dataset generation and splitting
+- Customizable MLP architecture (layers, nodes, activations)
+- Real-time training and validation visualization
+- Performance metrics and plots
+- Weight and bias visualization
+- Network architecture visualization
+- Confusion matrix and classification report on test data
+
+## Usage
+
+1. Start by configuring your dataset parameters and data split
+2. Design your MLP architecture (choose layers, nodes, and activations)
+3. Confirm the network to visualize the architecture
+4. Train the model and observe both training and validation metrics
+5. Test the model on unseen data and analyze the confusion matrix and classification metrics
+
+## Requirements
+
+- Python 3.8+
+- See requirements.txt for package dependencies (including: streamlit, numpy, pandas, scikit-learn, matplotlib, torch, networkx, seaborn)