Semi-Supervised DL

Semi-Supervised GANs for Melanoma Detection you own this product

This project is part of the liveProject series Semi-Supervised Deep Learning with GANs for Melanoma Detection
intermediate Python • intermediate deep learning • beginner PyTorch • basics of neural networks
skills learned
generative modeling with unsupervised GANs • semi-supervised learning with GANs
Olga Petrova
1 week · 8-10 hours per week · INTERMEDIATE

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The project goes straight to the point and provides focused hints that really help.

Mikael Dautrey, Executive, Isitix
Look inside
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.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

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.


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:

  • Intermediate Python
  • Basics of PIL
  • Basics of Matplotlib
  • Basics of NumPy
  • Beginner PyTorch
  • 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

you will learn

In this liveProject, you will learn important deep learning tools and techniques that are highly transferable to a wide range of machine learning roles, especially in the field of computer vision.

  • Data augmentation built into the image preprocessing pipeline
  • PyTorch and deep learning on the GPU
  • Generative modeling with unsupervised generative adversarial networks
  • Semi-supervised learning with GANs for image classification


You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
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book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.

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