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www.gregreda.com | ||
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teddykoker.com
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| | | | | A few posts back I wrote about a common parameter optimization method known as Gradient Ascent. In this post we will see how a similar method can be used to create a model that can classify data. This time, instead of using gradient ascent to maximize a reward function, we will use gradient descent to minimize a cost function. Lets start by importing all the libraries we need: | |
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www.nyckel.com
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| | | | | A confusion matrix is a great way of visualizing your machine learning classification models. This tool makes it easy to build them. | |
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gouthamanbalaraman.com
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| | | | | Discusses simulation of the Hull White interest rate term structure model in QuantLib Python | |
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neuralnetworksanddeeplearning.com
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