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www.machinedlearnings.com | ||
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www.paepper.com
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| | | | | Recent advances in training deep neural networks have led to a whole bunch of impressive machine learning models which are able to tackle a very diverse range of tasks. When you are developing such a model, one of the notable downsides is that it is considered a "black-box" approach in the sense that your model learns from data you feed it, but you don't really know what is going on inside the model. | |
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windowsontheory.org
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| | | | | Previous post: ML theory with bad drawings Next post: What do neural networks learn and when do they learn it, see also all seminar posts and course webpage. Lecture video (starts in slide 2 since I hit record button 30 seconds too late - sorry!) - slides (pdf) - slides (Powerpoint with ink and animation)... | |
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yasha.solutions
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| | | | | A loss function, also known as a cost function or objective function, is a critical component in training machine learning models, particularly in neural networks and deep learning... | |
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ayvlasov.wordpress.com
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| | | Recent debates on possibility of quantum computer provoked a specific prize. Between all, Scott Aaronson wrote: [...] whether scalable quantum computing is possible is a question about the laws of physics. It's perfectly conceivable that future developments in physics would conflict with scalable quantum computing, in the same way that relativity conflicts with faster-than-light communication,... | ||
