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Deep Learning is a sub-field of machine learning that focuses on learning features from data through multiple layers of abstraction. These features are learned with little human domain knowledge and have dramatically improved state of the art in many applications from computer vision to speech recognition. Deep learning helps researchers analyze large medical data and detect and diagnose various diseases at a faster rate. In this workshop, principles and methods of neural network and state of the art deep learning architecture like CNN and its application for medical image segmentation and classification will be covered along with research issues in deep learning based methods for medical applications. The workshop will cover working of Deep Learning algorithms on PyTorch platform as well as dive into the reality of applying these algorithms for real time applications.
Pre-requisite: Basics of Python
Workshop with hands on experience
• Introduction to Machine Learning
• Linear and logistic regression
• Deep Neural Networks (DNN)
• Convolutional Neural Networks(CNN)
• Optimization and general practices of training a NN/CNN
• Hands on Introduction to PyTorch
• Detection and Segmentation models
• Hands on with Classification Network and Segmentation Networks for Biomedical Applications
Eg:Liver Segmentation from CT scan
Paper Presentation Topics
DL,ML,DNN and CNN
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