Convolutional Neural Networks

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    You will learn more about image classification, covering several core deep learning concepts that are necessary to get good performance: what a learning rate is and how to choose a good one, how to vary your learning rate over time, how to improve your model with data augmentation (including test-time augmentation). We also share practical tips (such as training on smaller images), an 8-step process to train a world-class image classifier, and more information on your hardware setup (including crestle, paperspace, and AWS as options).

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