This repository contains assignments completed for the course "Neural Network".
"Neural Network" is a course that explores various natural language processing (NLP) techniques and machine learning models for understanding and generating text. The course covers topics such as neural language models, sequence-to-sequence models, attention mechanisms, and transformer architectures.
This repository is organized into separate folders for each assignment. Each assignment folder contains the following:
- Assignment Description: A README or PDF file describing the requirements and objectives of the assignment.
- Solution Code: Python scripts or Jupyter notebooks containing the code implementation for the assignment tasks.
- Datasets: Any datasets required for completing the assignment tasks.
- Results: Optionally, folders or files containing the results, such as trained models, evaluation metrics, or generated outputs.
To get started with the assignments, follow these steps:
- Clone this repository to your local machine:
git clone https://github.com/Yuvaraj1Aravindan/llm_assignments.git-
Navigate to the specific assignment folder you are interested in.
-
Read the assignment description file to understand the requirements.
-
Use the provided solution code and datasets to complete the assignment tasks.
Contributions to this repository are welcome. If you find any issues or have suggestions for improvements, feel free to open an issue or submit a pull request.
This project is licensed under the MIT License - see the LICENSE file for details.
Special thanks to the course instructor https://www.linkedin.com/in/yuvaraj-aravindan-93689a25 for mentoring and providing the assignments and resources for learning the Neural Networks.