Explainable AI

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This project is part of the liveProject series Transformers and Explainable AI for Computer Vision
prerequisites
intermediate Python (particularly TensorFlow or PyTorch) • intermediate knowledge of image classification and segmentation principles • basic knowledge of web applications • basic knowledge of deep learning pipelines and explainability
skills learned
build a web interface for MRI classification and segmentation using Gradio • run a vision transformer model for MRI classification and a Segformer or Maskformer model for MRI segmentation via the interface • implement Grad-Cam and LIME to explain transformer predictions
Anuradha Kar
1 week · 4-6 hours per week · INTERMEDIATE
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In this liveProject, you’ll take on the role of an engineer at AISoft, where you'll be part of two dynamic teams: MLOps and Core-ML. On the MLOps team, you'll utilize software engineering techniques to ensure your models are not only accurate but also scalable, reliable, and maintainable. You’ve been assigned two important support tasks for the Core-ML team.First, you'll utilize the Gradio Python library to create an interactive web user interface that runs MRI classification and segmentation transformer models in the background, and then you'll build a pipeline that provides interpretability to the decisions of a construction vehicle classification model.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

project author

Anuradha Kar
Anuradha Kar is a researcher at the Institut Pasteur in Paris, working on deep learning applications in drug discovery. Before this, she worked at the Paris Brain Institute on applying attention-based deep learning models to understanding the evolution of Alzheimer's disease and at École normale supérieure de Lyon in France on deep learning-based analysis of 3D bio-image datasets. She has a Ph.D. in electrical engineering from the National University of Ireland, Galway. In 2021, she published a liveProject series with Manning Publications titled Transfer Learning for Dicom Image Classification.

prerequisites

This liveProject series is aimed at intermediate-level Python programmers who already know the basics of deep learning and computer vision.


TOOLS
  • Intermediate Python
  • Intermedite Jupyter Notebook
  • Intermediate TensorFlow
  • Intermediate PyTorch
  • Intermediate OpenCV

TECHNIQUES
  • Intermediate levels of deep learning, image classification, and segmentation
  • Intermediate level concepts of software user interface design
  • Preliminary ideas regarding machine learning model explainability
  • Intermediate levels of data science

you will learn

In this liveProject, you’ll learn vital engineering skills for supporting machine learning development and converting ML models into real-world applications.


  • Utilize the Gradio library for web UI creation.
  • Integrate trained transformer models into the Gradio interface backend to process user input.
  • Extract vision-based explanations using Grad-CAM and LIME for transformer model decisions.
  • Apply explainability techniques to multiclass image classification tasks.

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book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.

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