The availability of models and their weights for anyone to download enables a broader range of developers to innovate and create.
In this course, you’ll select open source models from Hugging Face Hub to perform NLP, audio, image and multimodal tasks using the Hugging Face transformers library. Easily package your code into a user-friendly app that you can run on the cloud using Gradio and Hugging Face Spaces.
You will:
1. Use the transformers library to turn a small language model into a chatbot capable of multi-turn conversations to answer follow-up questions.
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The availability of models and their weights for anyone to download enables a broader range of developers to innovate and create.
In this course, you’ll select open source models from Hugging Face Hub to perform NLP, audio, image and multimodal tasks using the Hugging Face transformers library. Easily package your code into a user-friendly app that you can run on the cloud using Gradio and Hugging Face Spaces.
You will:
1. Use the transformers library to turn a small language model into a chatbot capable of multi-turn conversations to answer follow-up questions.
The availability of models and their weights for anyone to download enables a broader range of developers to innovate and create.
In this course, you’ll select open source models from Hugging Face Hub to perform NLP, audio, image and multimodal tasks using the Hugging Face transformers library. Easily package your code into a user-friendly app that you can run on the cloud using Gradio and Hugging Face Spaces.
You will:
1. Use the transformers library to turn a small language model into a chatbot capable of multi-turn conversations to answer follow-up questions.
2. Translate between languages, summarize documents, and measure the similarity between two pieces of text, which can be used for search and retrieval.
3. Convert audio to text with Automatic Speech Recognition (ASR), and convert text to audio using Text to Speech (TTS).
4. Perform zero-shot audio classification, to classify audio without fine-tuning the model.
5. Generate an audio narration describing an image by combining object detection and text-to-speech models.
6. Identify objects or regions in an image by prompting a zero-shot image segmentation model with points to identify the object that you want to select.
7. Implement visual question answering, image search, image captioning and other multimodal tasks.
8. Share your AI app using Gradio and Hugging Face Spaces to run your applications in a user-friendly interface on the cloud or as an API.
The course will provide you with the building blocks that you can combine into a pipeline to build your AI-enabled applications!
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Activities
Review fundamentals of signal processing for audio applications
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A strong foundation in signal processing is essential for audio-related tasks. This activity refreshes your knowledge, ensuring a solid understanding.
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Signal Processing
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Read through the Hugging Face documentation on audio processing
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Review basic concepts of signal processing, such as sampling, quantization, and filtering
Practice language translation using the transformers library
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Hands-on practice with the transformers library helps solidify your understanding of language translation techniques.
Show steps
-
Translate a short paragraph from English to French in Python using the transformers library
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Explore different translation models and compare their accuracy
Build an image classification model with Hugging Face Hub
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This activity provides practical experience in model deployment and serving, enhancing your understanding of the model deployment process.
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-
Follow the Hugging Face tutorial on deploying a model to Hugging Face Hub
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Deploy your own image classification model to Hugging Face Hub
One other activity
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Develop a multimodal AI application using Gradio
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Creating a multimodal AI application allows you to apply your knowledge in a practical project, solidifying your understanding of multimodal AI.
Show steps
-
Design the user interface for your application using Gradio
-
Integrate multiple AI models into your application
Review fundamentals of signal processing for audio applications
Show steps
A strong foundation in signal processing is essential for audio-related tasks. This activity refreshes your knowledge, ensuring a solid understanding.
Browse courses on
Signal Processing
Show steps
- Read through the Hugging Face documentation on audio processing
- Review basic concepts of signal processing, such as sampling, quantization, and filtering
Practice language translation using the transformers library
Show steps
Hands-on practice with the transformers library helps solidify your understanding of language translation techniques.
Show steps
- Translate a short paragraph from English to French in Python using the transformers library
- Explore different translation models and compare their accuracy
Build an image classification model with Hugging Face Hub
Show steps
This activity provides practical experience in model deployment and serving, enhancing your understanding of the model deployment process.
Show steps
- Follow the Hugging Face tutorial on deploying a model to Hugging Face Hub
- Deploy your own image classification model to Hugging Face Hub
Develop a multimodal AI application using Gradio
Show steps
Creating a multimodal AI application allows you to apply your knowledge in a practical project, solidifying your understanding of multimodal AI.
Show steps
- Design the user interface for your application using Gradio
- Integrate multiple AI models into your application
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