Course Outline
Introduction
- Overview of NLP and its applications
- Introduction to Hugging Face and its key features
Setting up a working environment
- Installing and configuring Hugging Face
Understanding the Hugging Face Transformers library and Transformer Models
- Exploring the Transformers library structure and functionalities
- Overview of various Transformer models available in Hugging Face
Utilizing Hugging Face Transformers
- Loading and using pretrained models
- Applying Transformers for various NLP tasks
Fine-Tuning a Pretrained Model
- Preparing a dataset for fine-tuning
- Fine-tuning a Transformer model on a specific task
Sharing Models and Tokenizers
- Exporting and sharing trained models
- Utilizing tokenizers for text processing
Exploring Hugging Face Datasets Library
- Overview of the Datasets library in Hugging Face
- Accessing and utilizing pre-existing datasets
Exploring Hugging Face Tokenizers Library
- Understanding tokenization techniques and their importance
- Leveraging tokenizers from Hugging Face
Carrying out Classic NLP Tasks
- Implementing common NLP tasks using Hugging Face
- Text classification, sentiment analysis, named entity recognition, etc.
Leveraging Transformer Models for Addressing Tasks in Speech Processing and Computer Vision
- Extending the use of Transformers beyond text-based tasks
- Applying Transformers for speech and image-related tasks
Troubleshooting and Debugging
- Common issues and challenges in working with Hugging Face
- Techniques for troubleshooting and debugging
Building and Sharing Your Model Demos
- Designing and creating interactive model demos
- Sharing and showcasing your models effectively
Summary and Next Steps
- Recap of key concepts and techniques learned
- Guidance on further exploration and resources for continued learning
Requirements
- A good knowledge of Python
- Experience with deep learning
- Familiarity with PyTorch or TensorFlow is beneficial but not required
Audience
- Data scientists
- Machine learning practitioners
- NLP researchers and enthusiasts
- Developers interested in implementing NLP solutions
Testimonials (3)
This is one of the best hands-on with exercises programming courses I have ever taken.
Laura Kahn
Course - Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
I did like the exercises.
Office for National Statistics
Course - Natural Language Processing with Python
Los ejemplos y la paciencia del instructor