Introduction to Tensorflow-NLP-tutorial
The Tensorflow-NLP-tutorial project is an extensive series of tutorials aimed at guiding users through Natural Language Processing (NLP) using Tensorflow 2.0. This collection is part of a wider educational resource for individuals interested in diving into the realm of NLP, facilitated by deep learning technologies. Notably, those who prefer PyTorch can explore a parallel set of tutorials via a helpful link.
This project is essentially the repository of tutorials linked to the "Introduction to Natural Language Processing using Deep Learning", hosted on Wikidocs. It uses Tensorflow 2.0+, a powerful framework for building deep learning models. Those eager for a comprehensive theoretical understanding can also access a 1,000-page e-Book, free of charge, to complement the practical tutorials.
Learning via Colab
For those keen on hands-on practice, the project provides easy access to practice files. Each Python (.py) file in the repository is directly linked to a corresponding Google Colab notebook. These files are automatically converted from Jupyter Notebook (.ipynb) format, allowing users to engage in practical exercises without needing to install Python locally. By simply opening the links through a Chrome browser, learners can begin their NLP journey immediately using Google's collaborative environment.
Project Updates
The Tensorflow-NLP-tutorial has been consistently updated with new content and features since its inception, ensuring that learners have access to the latest practices in NLP:
- January 1, 2022: Opening of the project on GitHub.
- January 3, 2022: Addition of Chapter 18, featuring practical exercises using BERT for text classification, named entity recognition, question answering, natural language inference, and chatbots with SBERT.
- January 18, 2022: Addition of Chapter 22, including exercises with KoGPT-2 for text generation, chatbots, and text classification.
- January 20, 2022: Expansion of Chapter 19, introducing KeyBERT for keyword extraction using BERT.
- February 16, 2022: Further expansion of Chapter 19, with the introduction of Combined Topic Models (CTM) for complex topic modeling using BERT.
- February 24, 2022: Enrichment of Chapter 19 with CTM for the Korean language and BERTopic for both English and Korean.
- February 18, 2024: Release of Chapter 23, featuring exercises in fine-tuning Large Language Models (LLM).
Overall, this project provides both a rich repository of practical guides and theoretical insights, making it an invaluable resource for anyone looking to explore NLP with Tensorflow.