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Sloth or Pastry? Using PyTorch and Deep Learning for Image Classification

In this session, using DataCamp Workspace we'll learn all about loading custom datasets into PyTorch and using transfer learning to perform an image processing task using a mostly-pretrained model, which we'll fine-tune.
Jun 2023
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In the age of deep learning, data scientists and machine learning engineers seldom create and train neural networks from scratch. A big chunk of what goes into performing a machine learning task, however, is collecting, preparing, and loading data to feed into a model. 

In this session, using DataLab we'll learn all about loading custom datasets into PyTorch and using transfer learning to perform an image processing task using a mostly-pretrained model, which we'll fine-tune.

We'll be using computer vision to answer the internet-popular question: is it a sloth or a pain au chocolat? This is a binary image classification task. 

Key takeaways

  • Machine learning engineers seldom train models from scratch
  • Learning to load data is an important building block for deep learning in PyTorch
  • Models trained on billions of images are available at our disposal for transfer learning
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