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www.kmjn.org | ||
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tomhume.org
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| | | | | I don't remember how I came across it, but this is one of the most exciting papers I've read recently. The authors train a neural network that tries to identify the next in a sequence of MNIST samples, presented in digit order. The interesting part is that when they include a proxy for energy usage in the loss function (i.e. train it to be more energy-efficient), the resulting network seems to exhibit the characteristics of predictive coding: some units seem to be responsible for predictions, others for encoding prediction error. | |
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www.chrisritchie.org
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polukhin.tech
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| | | | | As the field of Deep Learning continues to grow, the demand for efficient and lightweight neural networks becomes increasingly important. In this blog post, we will explore six lightweight neural network architectures. | |
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zserge.com
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| | | Neural network and deep learning introduction for those who skipped the math class but wants to follow the trend | ||
