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
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.
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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