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

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
Tuesday May 30, 11AM ET
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Description

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 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.

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. 

Presenter Bio

Maham Khan Headshot
Maham KhanSenior Data Science Content Developer at DataCamp

Maham is a Senior Data Science Content Developer at DataCamp, on a mission to make data skills accessible for everyone.

Before joining DataCamp, she worked as a Data Scientist at the World Bank, exploring applications of data science for disaster risk reduction, poverty alleviation and climate change mitigation.

She has a background in Experimental Psychology and Philosophy from the University of Oxford, and Urban Data Science from New York University.

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