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When Kaggle started the cats vs. Fine-tuning consist in starting from a trained network, then re-training it on a new dataset using very small weight updates. To further improve our previous result, we can try to "fine-tune" the last convolutional block of the VGG16 model alongside the top-level classifier. Since we only have few examples, our number one concern should be overfitting. If you have any comment about this post or any suggestion about future topics to cover, you can reach out on Twitter. This is very similar to the architectures that Yann LeCun advocated in the s for image classification with the exception of ReLU.

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We end the model with a single unit and a sigmoid activation, which is perfect for a binary classification.

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But let's take a look at how we record the bottleneck features using image data generators:. I guess that at least entrants weren't using deep learning ;. Such a network would have already learned features that are useful for most computer vision problems, and leveraging such features would allow us to reach a better accuracy than any method that would only rely on the available data.

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