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Table Of Contents
- Introduction
- Future Of AI: Open Source Models with Hugging Face And Andrew Ng
- What we will learn in this course?
- Summary
- References
Introduction
In this post, we give an overview of new course introduce by hugging Face and Andrew Ng, Open Source Models with Hugging Face.
Future Of AI: Open Source Models with Hugging Face And Andrew Ng
A Guide to Discovering, Filtering, and Deploying with Hugging Face Hub, Transformers, Gradio, and Hugging Face Spaces
- Find and filter open source models on Hugging Face Hub based on task, rankings, and memory requirements.
- Write just a few lines of code using the transformers library to perform text, audio, image, and multimodal tasks.
- Easily share our AI apps with a user-friendly interface or via API and run them on the cloud using Gradio and Hugging Face Spaces.
What we will learn in this course?
The availability of models and their weights for anyone to download enables a broader range of developers to innovate and create.
In this course, we will learn select open source models from Hugging Face Hub to perform NLP, audio, image and multimodal tasks using the Hugging Face transformers library.
Easily package our code into a user-friendly app that we can run on the cloud using Gradio and Hugging Face Spaces.
We will:
- Use the transformers library to turn a small language model into a chatbot capable of multi-turn conversations to answer follow-up questions.
- Translate between languages, summarize documents, and measure the similarity between two pieces of text, which can be used for search and retrieval.
- Convert audio to text with Automatic Speech Recognition (ASR), and convert text to audio using Text to Speech (TTS).
- Perform zero-shot audio classification, to classify audio without fine-tuning the model.
- Generate an audio narration describing an image by combining object detection and text-to-speech models.
- Identify objects or regions in an image by prompting a zero-shot image segmentation model with points to identify the object that we want to select.
- Implement visual question answering, image search, image captioning and other multimodal tasks.
- Share our AI app using Gradio and Hugging Face Spaces to run our applications in a user-friendly interface on the cloud or as an API.
The course will provide we with the building blocks that we can combine into a pipeline to build our AI-enabled applications!
Summary
Embark on a transformative journey with the ‘Open Source Models with Hugging Face’ course by Andrew Ng.
Explore the vast landscape of available models, empowering developers to innovate across NLP, audio, image, and multimodal domains.
Through hands-on learning with the Hugging Face transformers library, you’ll delve into creating a chatbot, language translation, summarization, audio-to-text conversion, zero-shot audio classification, and more.
Learn to fuse object detection with text-to-speech models for generating audio narrations of images and master tasks like visual question answering and image captioning.
Elevate your applications by packaging code into user-friendly apps deployable on the cloud using Gradio and Hugging Face Spaces.
This course equips you with the essential building blocks to craft your AI-enabled applications and drive innovation in the world of open source models.
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