This repository contains a collection of Natural Language Processing (NLP) tutorials based on the book "Getting Started with Deep Learning Using PyTorch" published on Wikidocs. All tutorials are implemented using the PyTorch deep learning framework.
Theoretical explanations and foundations of the code are covered extensively in a free e-book, which spans approximately 950 pages. If you're interested in the concepts behind the code or wish to deepen your understanding, please refer to the e-book below:
📖 e-Book: Getting Started with Deep Learning Using PyTorch
For hands-on practice, each .py tutorial script includes a link to a corresponding Google Colab notebook. These .py files are automatically converted from .ipynb (Jupyter notebook) files for easier code sharing.
To run the tutorials without any local Python installation:
- Open the
.pyfile. - Click the Colab link provided at the top.
- The notebook will open in your browser (preferably Chrome), where you can run it directly.
Although you can run the notebooks in Colab without installing anything, if you wish to run the code locally, make sure to install:
- Python 3.x
- PyTorch
- Jupyter Notebook (optional, for running
.ipynbfiles)
Each directory or file in this repository represents an individual tutorial, covering a specific NLP topic or model, including but not limited to:
- Text classification
- Sentiment analysis
- Word embeddings
- RNNs, LSTMs, Transformers
- Language modeling
Feel free to fork, use, and improve the content. Pull requests are welcome!