In this course, you will :
- Explore the Hugging Face artificial intelligence library, paying special attention to natural language processing (NLP) and computer vision. You'll start by learning about Hugging Face's approach to deep learning, with a focus on transformers.
- Discover Hugging Face's pipeline API model and apply it to various NLP tasks like classification, summarization, question answering, and more.
- You will then proceed with a new set of Hugging Face pipelines for computer vision tasks such as object detection and segmentation.
- A familiarity with Hugging Face and their library of machine learning models
- A working knowledge of Hugging Face’s pipeline APIs and their applications
- The ability to apply Hugging Face models to generate and read text using natural language processing
- The ability to apply Hugging Face models to computer vision tasks
- Hands-on experience implementing Hugging Face models using Python and PyTorch
- Introduction to the Course
- Hugging Face
- Introduction to NLP Inference
- Text and Token Classification
- Text Summarization
- Machine Translation
- Question Answering
- Text GenerationSimilarity
- Fill Mask
\3. Computer Vision
- Image Classification
- Object Detection