Conditional Probability


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Maxpooling vs minpooling vs average pooling

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Pooling is performed in neural networks to reduce variance and computation complexity. Many times, beginners blindly use a pooling method without knowing the reason for using it. Here is a comparison of three basic pooling methods that are widely used. The three types of pooling operations are: Max pooling: The maximum pixel value of the batch is selected. Min pooling: The minimum pixel value of the batch is selected. Average pooling: The average value of all the pixels in the batch is selected. The batch here means a group of pixels of size equal to the filter size which is decided based on the size of the image. In the following example, a filter of 9x9 is chosen. The output of the pooling method varies with the varying value of the filter size. The operations are illustrated through the following figures. Average, Max and Min pooling of size 9x9 applied on an image We cannot say that a particular pooling method is better over others generally. The choice of pooling operation is made
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Percentiles, Deciles, and Quartiles

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Percentiles Percentile: the value below which a percentage of data falls. Example: You are the fourth tallest person in a group of 20 80% of people are shorter than you: That means you are at the  80th percentile . If your height is 1.85m then "1.85m" is the 80th percentile height in that group. In Order Have the data  in order , so you know which values are above and below. To calculate percentiles of height: have the data in height order (sorted by height). To calculate percentiles of age: have the data in age order. And so on. Grouped Data When the data is grouped: Add up all percentages  below  the score, plus  half  the percentage  at  the score. Example: You Score a B! In the test 12% got D, 50% got C, 30% got B and 8% got A You got a B, so add up all the 12% that got D, all the 50% that got C, half of the 30% that got B, for a total percentile of 12% + 50% + 15% =  77% In other words you did "as well or better than
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Understand the Softmax Function in Minutes

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Learning machine learning? Specifically trying out neural networks for deep learning? You likely have run into the  Softmax function, a wonderful  activation function  that turns numbers aka logits into probabilities that sum to one. Softmax function outputs a vector that represents the probability distributions of a list of potential outcomes.  It’s also a core element used in  deep learning   classification  tasks. We will help you understand the Softmax function in a beginner friendly manner by showing you exactly how it works — by coding your very own Softmax function in python. If you are implementing Softmax in Pytorch and you already know Pytorch well, scroll down to the Deep Dive section and grab the code. This article has gotten really popular: 5800+ claps. It is updated constantly. Latest update Jan 2020 added a TL;DR section for busy souls. Dec 2019 (Softmax with Numpy Scipy Pytorch functional. Visuals indicating the location of Softmax function in Neural Network architectur
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