Welcome to the Applied Natural Language Processing Course (IE-7500) at Northeastern University! This repository contains the labs and reading materials designed to help you grasp the concepts and applications of Natural Language Processing. Throughout this course, you'll explore foundational and advanced techniques, gain practical experience through hands-on labs, and delve into various generative models.
Covers the fundamentals of Natural Language Processing (NLP) and its diverse applications in various engineering domains. Students will gain a comprehensive understanding of essential NLP concepts and key algorithms. The course highlights how NLP can address engineering challenges in fields such as transportation, civil engineering, manufacturing, healthcare, business, commerce, and other selected engineering areas. Through a substantial course project, students will solidify their skills by applying NLP to solve practical engineering problems.
- IE 7300
- Proficiency with Python programming
- Experience with TensorFlow
- Knowledge in Neural networks, Machine Learning, Linear Algebra, Probability, and Statistics
The labs in this repository are designed to provide hands-on experience with the concepts covered in the course. Each lab includes detailed instructions, code samples, and exercises to help you apply what you've learned in a practical setting. Topics include:
- Neural Networks (FeedForward NNs, Optimization, Regularization, Dropout, Batch-normalization, CNNs, RNNs, LSTM)
- Transformers (Transformers, BERT, GPT, Performer)
To get started with the labs and exercises in this repository, please follow these steps:
- Clone this repository to your local machine.
- Navigate to the specific lab you are interested in.
- Read the lab instructions and review any accompanying documentation.
- Follow the provided code samples and examples to complete the lab exercises.
- Feel free to explore, modify, and experiment with the code to deepen your understanding.
For more detailed information on each lab and prerequisites, please refer to the lab's README or documentation.
We welcome contributions from students and instructors to improve and expand the materials in this repository. If you have suggestions, bug reports, or would like to add new content, please submit a pull request or open an issue. Make sure to follow the contribution guidelines outlined in the CONTRIBUTING.md file.
The reading materials of this repository were collected from the internet under the Creative Commons License. These materials are intended for educational purposes and to enhance your learning experience.
This repository is licensed under the MIT License. For more details, please refer to the LICENSE file.
We hope you find these resources helpful and enjoy your journey into the world of Natural Language Processing! For any questions or support, please contact the course instructor or teaching assistant.