![]() Fine-tuning a network with transfer learning is usually much faster and easier than training a network with randomly initialized weights from scratch. You can take a pretrained network and use it as a starting point to learn a new task. Transfer learning is commonly used in deep learning applications. The network takes an image as input and outputs a label for the object in the image together with the probabilities for each of the object categories. ![]() The network has learned rich feature representations for a wide range of images. This example shows how to fine-tune a pretrained AlexNet convolutional neural network to perform classification on a new collection of images.ĪlexNet has been trained on over a million images and can classify images into 1000 object categories (such as keyboard, coffee mug, pencil, and many animals).
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