Update README.md to reflect the completion of Part 9: Multi-Head Attention, and adjust the status of upcoming sections in the educational series roadmap for clarity and consistency.

Update links for Part 8: Causal Attention across README.md, Jupyter notebook, and Python module to ensure consistency and accuracy in documentation. The new links direct to the correct Medium article.

Add Part 8: Causal Attention (Masked Self-Attention) implementation, including updates to README.md, new medium article, Jupyter notebook, and Python module. This part addresses the future leakage problem in autoregressive models by implementing causal masking, ensuring proper attention behavior during text generation.

Update README.md to reflect the completion of Parts 6 (The Attention Mechanism) and 7 (Self-Attention with Trainable Weights), and adjust the status of upcoming sections, ensuring clarity in the educational series roadmap.

Update Part 7: Self-Attention with Trainable Weights to include the correct article link across README, notebook, and Python module, ensuring consistency in documentation.

Add Part 7: Self-Attention with Trainable Weights

Update README.md and related files to reflect the correct link for Part 6 (The Attention Mechanism). Adjust notebook and Python module to include the updated article link, ensuring consistency across documentation.

lecture 16

Update README.md, medium article, and Python module to reflect the correct link for Part 5 (Complete Data Preprocessing Pipeline). Adjust notebook to include the updated article link and add a blank markdown cell for future content.

Update README.md to include the completion of Part 5 (Complete Data Preprocessing Pipeline) and adjust the roadmap status for upcoming parts, ensuring clarity in the series progression.

Update README.md to reflect the completion of Part 4 (Token Embeddings & Positional Encoding) and adjust the status of the Self-Attention Mechanism. Update links in the notebook and Python module to point to the correct Medium article for Part 4.

Refactor vector embedding demonstration notebook to improve clarity and organization, enhancing the user experience for word vector operations and similarity calculations.

Add Jupyter notebook for vector embedding demonstration, showcasing word vector loading, similarity calculations, and vector operations using Gensim.

Add torch to requirements.txt for enhanced model support

Update README.md to reflect the completion status of Part 3 (Data Pipeline) and adjust the roadmap for upcoming parts, including changes to status indicators and links.