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senthil.learntosolveit.com | ||
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studywolf.wordpress.com
When plotting means and confidence intervals, sometimes the mean lines are hard to see and it's nice to have included in your legend the color of the confidence interval shading. It turns out this is a bit of a chore in Matplotlib, but building off of their online examples you can get something that looks... |
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glowingpython.blogspot.com
And here's a function to plot a compact calendar with matplotlib: import calendar import numpy as np from matplotlib.patches import Rectan... |
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mika-s.github.io
One thing that can be hard to get right is the mapping from to radians to to radians. Or to to -180° to 180°. I've been unable to find information about it with Google (except for the functions that comes with Matlab), so I decided to share a function I've made that does the transformation. In C#: void TransformToPipi(double inputAngle, out double outputAngle, out int revolutions) { revolutions = (int)((inputAngle + Math.Sign(inputAngle) * Math.PI) / (2 * Math.PI)); outputAngle = (inputAngle + Math.Sign(inputAngle) * Math.PI) % (2 * Math.PI) - (Math.Sign(Math.Sign(inputAngle) + 2 * (Math.Sign(Math.Abs(((inputAngle + Math.PI) % (2 * Math.PI)) / (2 * Math.PI))) - 1))) * Math.PI; } The function takes an angle in radians as input and outputs an angle between and... |
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programmathically.com
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 [] |
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