click to
look inside
Look inside

Semi-Supervised Deep Learning with GANs for Melanoma Detection

prerequisites
intermediate Python • intermediate NumPy • beginner PyTorch • basics of deep learning (CNNs)
skills learned
generative modeling • transfer learning • image classification with deep CNNs • semi-supervised learning with GANs
Olga Petrova
6 weeks · 6-10 hours per week · INTERMEDIATE

placing your order...

Don't refresh or navigate away from the page.
liveProject liveProjects give you the opportunity to learn new skills by completing real-world challenges in your local development environment. These self-paced projects also come with full liveBook access to select books for 90 days plus permanent access to other select Manning products. $32.49 $49.99 you save: $17 (35%) self-paced learning
FREE domestic shipping on orders of three or more print books
Semi-Supervised Deep Learning with GANs for Melanoma Detection (liveProject) added to cart
continue shopping
go to cart
Look inside
In this liveProject, you’ll take on the role of a computer vision engineer creating a proof-of-concept for a mobile app with world-changing potential. The goal of the app is to capture a picture of a skin mole, and then calculate the likelihood of it being a cancerous melanoma in need of medical intervention. Your mission is to build a model that can perform this image classification using low-resolution photos from a phone’s camera. You are provided with a training dataset, however, only a small fraction of the images have been labeled. To overcome this challenge, you will employ data augmentation techniques, transfer learning and semi-supervised learning with GANs to produce your final model.
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.

prerequisites

This liveProject is for intermediate Python programmers with some deep learning experience. Knowledge of PyTorch will be helpful. No prior experience in generative modeling, including GANs, is assumed. To begin this liveProject, you will need to be familiar with:

TOOLS
  • Basics of PIL
  • Basics of Matplotlib
  • Intermediate Python
  • Intermediate NumPy
  • Beginner PyTorch
TECHNIQUES
  • Classification as a machine learning task
  • Intermediate deep learning concepts such as convolutional neural networks

you will learn

In this liveProject, you will learn important deep learning tools and techniques for data augmentation, semi-supervised learning, and generative modeling. These techniques are highly transferable to a wide range of machine learning roles, especially in the field of computer vision.

  • Data augmentation built into the image pre-processing pipeline
  • Pytorch and deep learning on the GPU
  • Image classification using deep convolutional neural networks
  • Transfer learning
  • Generative modeling: variational autoencoders (VAE), generative adversarial networks (GANs)
  • Semi-supervised learning with GANs for image classification

features

Self-paced
You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Get Help
While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.
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.
RECENTLY VIEWED