Welcome to the Hugging Face course
By HuggingFace
Summary
## Key takeaways - **Hugging Face ecosystem overview**: This course teaches you all about the Hugging Face ecosystem: how to use the dataset and model hub as well as all our open source libraries. [00:05], [00:26] - **Three progressively advanced sections**: The Table of Contents is divided in three sections which become progressively more advanced. [00:05], [00:26] - **First two sections released**: At this stage, the first two sections have been released. The first teaches basics of using a Transformer model, fine-tuning it on your own dataset and sharing with the community. [00:26], [00:42] - **Third section spring 2022**: We are actively working on the last one and hope to have it ready for you for the spring of 2022. [00:42], [00:46] - **No tech knowledge for chapter one**: The first chapter requires no technical knowledge and is a good introduction to learn what Transformers models can do and how they could be of use to you or your company. [00:48], [01:03] - **Python, ML basics required later**: The next chapters require a good knowledge of Python and some basic knowledge of Machine Learning and Deep Learning. If you don't know what a training and validation set is or what gradient descent means, look at an introductory course. [01:03], [01:13]
Topics Covered
- Transformers Need No Prior Knowledge
- Master NLP with Framework Flexibility
Full Transcript
Welcome to the Hugging Face Course! This course has been designed to teach you all about the Hugging Face ecosystem: how to use the dataset and model hub as well as all our open source libraries. Here is the Table of Contents. As you can see, it's divided in three sections which become progressively more advanced. At this stage,
the first two sections have been released. The first will teach you the basics of how to use a Transformer model, fine-tune it on your own dataset and share the result with the community.
The second will dive deeper into our libraries and teach you how to tackle any NLP task. We are
actively working on the last one and hope to have it ready for you for the spring of 2022.
The first chapter requires no technical knowledge and is a good introduction to learn what Transformers models can do and how they could be of use to you or your company. The next chapters require a good knowledge of Python and some basic knowledge of Machine Learning and Deep Learning.
If you don't know what a training and validation set is or what gradient descent means, you should look at an introductory course such as the ones published by deeplearning.ai or fast.ai.
It's also best if you have some basics in one Deep Learning Framework (PyTorch or TensorFlow).
Each part of the material introduced in this course has a version in both those frameworks, so you will be able to pick the one you are most comfortable with. This is the team that developed this course. I'll now let each of the speakers introduce themselves briefly.
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