/explore
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ankane.org | ||
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colah.github.io
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| | | | | [AI summary] This article explains the architecture, mathematical formulation, and major breakthroughs of convolutional neural networks in the context of computer vision and pattern recognition. | |
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tcode2k16.github.io
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| | | | | a random blog about cybersecurity and programming | |
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coen.needell.org
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| | | | | In my last post on computer vision and memorability, I looked at an already existing model and started experimenting with variations on that architecture. The most successful attempts were those that use Residual Neural Networks. These are a type of deep neural network built to mimic specific visual structures in the brain. ResMem, one of the new models, uses a variation on ResNet in its architecture to leverage that optical identification power towards memorability estimation. | |
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marcospereira.me
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| | | In this post we summarize the math behind deep learning and implement a simple network that achieves 85% accuracy classifying digits from the MNIST dataset. | ||
