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transfer-learning-with-cnn

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The provided code demonstrates transfer learning by adapting a model trained using synthetic data to classify circles, squares, and triangles to classify new shapes like stars and pentagons. By fine-tuning a pre-trained model originally designed for a different task, the repository showcases how to efficiently adapt a model to a new domain.

  • Updated Oct 16, 2024
  • Python

An end-to-end multi-class image classification system(web app) that classifies 101 classes of food. I'll be implementing the popular CNN architecture while utilizing the full power of transfer learning to extract features and fine-tune layers. I'll also build an interactive UI using react-js and deploy the system.

  • Updated Jun 20, 2024
  • Jupyter Notebook

Photo sharing and photo storage services like to have location data for each photo that is uploaded. With the location data, these services can build advanced features, such as automatic suggestion of relevant tags or automatic photo organization, which help provide a compelling user experience.

  • Updated Oct 14, 2023
  • HTML
Capstone_AcousticEnvironment-DeepNeuralNet

Deep neural network model combining audio signal processing and pre-trained audio CNN achieved 90.1% adjusted accuracy (27.6% improvement) for classifying audio recording environment.

  • Updated Mar 25, 2023
  • Jupyter Notebook

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