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
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.
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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