In this liveProject, you’ll utilize PyTorch and powerful semi-supervised learning techniques to construct an advanced image classifier that can tell whether a 32x32 pixel photo of a mole is melanoma-positive—despite working with a very small labelled dataset. You’ll set up your image preprocessing pipeline, feed data into your PyTorch model, and then train a semi-supervised GAN model on both labeled and unlabeled datasets.
This project is designed for learning purposes and is not a complete, production-ready application or solution.
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project author
Olga Petrova
Olga Petrova is a machine learning engineer at Scaleway, a French cloud provider, where her focus lies on deep learning R&D. Previously, she has worked as a researcher in theoretical physics, looking into the applications of artificial intelligence to quantum systems. Olga has a Ph.D. from Johns Hopkins University, and a B.S. from Worcester Polytechnic Institute. She enjoys blogging about the latest advancements in AI.
prerequisites
This liveProject is for intermediate Python programmers with some deep learning experience. No prior experience in generative modeling, including GANs, is assumed. To begin this liveProject, you will need to be familiar with:
TOOLS
Intermediate Python
Basics of PIL
Basics of Matplotlib
Basics of NumPy
Beginner PyTorch
TECHNIQUES
Training and evaluating (supervised) deep learning models
Basics of neural networks
Intermediate deep learning concepts such as convolutional neural networks
Basics of linear algebra and statistics
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