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utkuufuk.com | ||
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www.arrsingh.com
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| | | | | Linear Regression predicts the value of a dependent variable (y) given one or more independent variables (x1, x2, x3...xn). In this case, y is continuous - i.e. it can hold any value. In many real world problems[1], however, we often want to predict a binary value instead | |
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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.ericekholm.com
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| | | | | Learning maximum likelihood estimation by fitting logistic regression 'by hand' (sort of) | |
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programmathically.com
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| | | Sharing is caringTweetIn this post, we develop an understanding of why gradients can vanish or explode when training deep neural networks. Furthermore, we look at some strategies for avoiding exploding and vanishing gradients. The vanishing gradient problem describes a situation encountered in the training of neural networks where the gradients used to update the weights [] | ||
