Transfer Learning

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prerequisites
intermediate Python • basics of deep learning • basics of Keras and OpenCV
skills learned
use transfer learning for training VGG16 and ResNet models in Keras • deploy VGG16 and ResNet models for training on DICOM images • train VGG16 and ResNet models on two different types of medical image datasets
Anuradha Kar
1 week · 4-6 hours per week · INTERMEDIATE
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In this liveProject, you’ll take pretrained VGG16 and ResNet models from the Python Keras library and train them further upon your medical image dataset of X-ray and CT scans. This transfer learning is a highly effective technique for quickly generating reliable machine learning models when you only have a small data set. You’ll experiment with the Keras loss functions to determine which are best for COVID image classification, and check your training and prediction times as a critical parameter of real-world applications.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

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 Notebook
  • Basics of Keras and OpenCV
TECHNIQUES
  • Basics of deep learning and image classification

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