Build a ResNet Model you own this product

prerequisites
intermediate Python • basics of deep learning • basics of Keras and OpenCV
skills learned
build a ResNet deep learning architecture with basic functional components in Keras • train ResNet model hyperparameters on two different types of medical image datasets (X-ray, CT) • tune ResNet model to improve performance
Anuradha Kar
1 week · 4-6 hours per week · INTERMEDIATE
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Look inside
In this liveProject, you’ll build a ResNet deep learning model from scratch to analyze medical imagery. A ResNet is a deep neural network model which uses "Residual blocks" and "skip connections" to reduce the need for very deep networks while still achieving high accuracy. You’ll then train your model on X-ray and CT datasets, and plot validation loss, and accuracies vs. epochs. You’ll build an important familiarity with the functional blocks of a DL model, how data must be formatted, and which layers to use to solve your problems.
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 Postdoctoral researcher at École normale supérieure de Lyon, and works in collaboration with the research institutes INRAE and INRIA in France. Her current research is on the application of deep learning algorithms for deriving quantitative information from microscopy image datasets. This is used by biologists to analyze cellular developmental processes in plants and animals. She has a PhD in electrical engineering from the National University of Ireland, Galway. Her research centers on vision sensors, artificial intelligence and computer vision. She has published on deep learning, human-computer interactions and sensor evaluation techniques.

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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