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7th KAUST-NVIDIA Workshop: Introduction to DL image classification with Keras
ImageNet, an image recognition benchmark dataset, helped trigger the modern AI explosion. In 2012, the AlexNet architecture (a deep convolutional-neural-network) rocked the ImageNet benchmark competition, handily beating the next best entrant. By 2014, all the leading competitors were deep learning based. Since then, accuracy scores continued to improve, eventually surpassing human performance.

In this hands-on tutorial we will build on this pioneering work to create our own neural-network architecture for image recognition. Participants will use the elegant Keras deep learning programming interface to build and train TensorFlow models for image classification tasks on the CIFAR-10 / MNIST datasets. We will demonstrate the use of transfer learning (to give our networks a head-start by building on top of existing, ImageNet pre-trained, network layers), and explore how to improve model performance for standard deep learning pipelines. We will use cloud-based interactive Jupyter notebooks to work through our explorations step-by-step. Once participants have successfully trained their custom model we will show them how to submit their model's predictions to Kaggle for scoring.
Participants are expected to have access to laptops/workstations and sign-up for free online cloud services (e.g., Google Colab, Kaggle). They may also need to download free, open-source software prior to arriving for the workshop.
Dec 2, 2020 09:00 AM
Dec 3, 2020 09:00 AM
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