Train a Supervised Learning Image Classifier

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prerequisites
intermediate Python • intermediate deep learning • beginner PyTorch • basics of neural networks
skills learned
PyTorch for deep learning on the GPU • image classification using deep convolutional neural networks • transfer learning to improve model accuracy
Olga Petrova
1 week · 8-10 hours per week · INTERMEDIATE
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In this liveProject, you’ll use the popular deep learning framework PyTorch to train a supervised learning model on a dataset of melanoma images. Your final product will be a basic image classifier that can spot the difference between cancerous and non-cancerous moles. You’ll create a custom dataset class and data loaders that can handle image preprocessing and data augmentation, and even improve the accuracy of your model with transfer learning.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

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 machine learning experience. 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
  • Classification as a machine learning task
  • Basics of model training, validation and testing
  • Monitoring training and spotting overfitting/underfitting
  • Basics of neural networks

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