Transfer Learning for Image Classification

Evaluate and Explain DL Models you own this product

This project is part of the liveProject series Transfer Learning for Dicom Image Classification
prerequisites
intermediate Python • basics of deep learning • basics of Keras and OpenCV
skills learned
make predictions using deep learning models using Keras • implement Grad-CAM visualization • implement deep learning model performance metrics • create a radar plot to display the performance of multiple models
Anuradha Kar
1 week · 4-6 hours per week · INTERMEDIATE
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Look inside
In this liveProject, you’ll implement model performance metrics that can test the effectiveness of your models. You’ll calculate accuracy, precision, F1 score and recall values from the classification results for an existing model, and then estimate the ROC curve and AUC value. Finally, you’ll create a Gradient Class Activation Map. This map can highlight features and regions in an image that the deep learning model finds important, and manually inspect whether the model is performing in the desired way.
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 is for intermediate Python programmers. To begin this liveProject, you will need to be familiar with:

TOOLS
  • Intermediate Python 3.x and Jupyter notebooks
  • Basics of Keras and OpenCV
TECHNIQUES
  • Basics of deep learning and image classification
  • VGG model architecture
  • ResNet model architecture

you will learn

In this liveProject, you’ll gain familiarity with medical image datasets and build deep neural networks to analyze them.

  • Using custom image data generators in Keras
  • Making predictions using deep learning models using Keras
  • Implementing Grad-CAM visualization
  • Implementing deep learning model performance metrics
  • Creating a radar plot to display the performance of multiple models

features

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You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
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While within the liveProject platform, get help from other participants and our expert mentors.
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For each step, compare your deliverable to the solutions by the author and other participants.
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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