📓 NLP Course Notebooks

This directory contains all the Jupyter notebooks for the Advanced NLP Classes. These notebooks provide hands-on experience with the concepts covered in the lectures.

⚙️ Setup Instructions

🔧 Prerequisites

Python 3.11 or higher
Poetry (for dependency management)

📦 Installation

We use Poetry to manage dependencies. Follow these steps to set up your environment:

1️⃣ Install Poetry

macOS / Linux:

curl -sSL https://install.python-poetry.org | python3 -

Windows:

(Invoke-WebRequest -Uri https://install.python-poetry.org -UseBasicParsing).Content | python -

2️⃣ Clone the repository and install dependencies

git clone https://github.com/agombert/AdvancedNLPClasses.git
cd AdvancedNLPClasses
poetry install

3️⃣ Install additional dependencies for notebooks

poetry add pandas numpy matplotlib scikit-learn spacy jupyter
poetry run python -m spacy download en_core_web_sm

4️⃣ Launch Jupyter Notebook

poetry run jupyter notebook

Navigate to the notebooks directory to access all the notebooks.

🛠️ Troubleshooting

If you encounter issues with the installation:

macOS: You might need to install Xcode command-line tools: xcode-select --install
Ubuntu: Ensure you have build essentials: sudo apt-get install build-essential
Windows: Make sure you have the Microsoft C++ Build Tools installed

📖 Table of Contents

🐍 Python Fundamentals (Session 1)

These notebooks cover the essential Python skills needed for NLP:

Python Types: Understanding Python's type system, from basic to advanced types
Python Classes: Object-oriented programming in Python
Python Dataframes: Working with pandas for data manipulation
Python NumPy: Numerical computing with NumPy
Python scikit-learn: Introduction to machine learning with scikit-learn

📝 NLP Techniques (Session 1)

Baseline with regexes and spaCy: Implementing simple but effective baseline approaches
TF-IDF: how to judge its quality?: Understanding and implementing TF-IDF
BM25: a better TF-IDF, judge through different metrics: Advanced information retrieval techniques

📝 Chapter 2: Neural Networks, Backpropagation & RNNs

Intro to Neural Nets & Backprop (NumPy): Implementing neural networks with NumPy
Simple RNN for Text Generation (Tiny Shakespeare): Generating text with a simple RNN
LSTM for Sequence Classification: Building an LSTM for sequence classification

📝 Chapter 3: Word Embeddings

Word2Vec from Scratch - with negative sampling: Implementing Word2Vec from scratch with negative sampling
Embedding Evaluation: Intrinsic and Extrinsic: Evaluating word embeddings using both intrinsic and extrinsic metrics
Classification with Embeddings: Using embeddings for classification tasks

🤝 Contributing

If you find errors or have suggestions for improving these notebooks, please open an issue or submit a pull request.

📄 License

These notebooks are provided for educational purposes as part of the Advanced NLP Classes at Barcelona School of Economics.