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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 | |
# **Embedding Dimension Visualizer** | |
An **Embedding Dimension Visualizer** is an interactive Streamlit tool designed for teaching and experimentation with modern transformer embeddings. It lets you: | |
* **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. | |
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. | |