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README.md

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-**App Description**
+---
+title: Learn Neural Networks
+emoji: 🚀
+colorFrom: red
+colorTo: red
+sdk: docker
+app_port: 8501
+tags:
+- streamlit
+pinned: false
+short_description: Logic Gate Learning with Neural Networks
+license: mit
+---
 
-An **Embedding Dimension Visualizer** is an interactive Streamlit tool designed for teaching and experimentation with modern transformer embeddings. It lets you:
+# Welcome to Streamlit!
 
-* **Tokenize** any input text using tiktoken or HuggingFace’s BPE tokenizer, showing each subword token and its ID.
-* **Visualize embeddings** by generating a demo embedding vector for every token.
-* **Compute and display sinusoidal positional encodings** (sin / cos) per token position.
-* **Combine embeddings + positional encodings** and present the final per-token vectors exactly as they’d be fed into attention heads.
-* **Expose theory** via an expandable section—complete with LaTeX formulas—covering tokenization, BPE, and the positional-encoding equations.
-* **Lock sliders** into read-only mode, so learners can observe values without accidentally altering them.
+Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
 
-This app is ideal for workshops, live demos, or self-study when you want a hands-on, visual understanding of how embeddings and positional information come together inside a transformer model.
+If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
+forums](https://discuss.streamlit.io).