Train a Supervised Learning Image Classifier

This project is part of the liveProject series Semi-Supervised Deep Learning with GANs for Melanoma Detection.
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.

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 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
  • 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.

    • Pytorch for deep learning on the GPU
    • Setting up an image preprocessing pipeline to feed data to a PyTorch model
    • Data augmentation built into the image preprocessing pipeline
    • Training a supervised learning classifier on labeled data
    • Image classification using deep convolutional neural networks
    • Testing a supervised learning model
    • Transfer learning for improving model accuracy

    features

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    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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