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The Mean Shift Algorithm and Motion Controls
I’ve written about the Mean Shift Algorithm before, but I never gave a practical application of it. I ultimately felt unsatisfied by the article, so I wanted to revisit the topic and apply it to a…
Read more at Towards Data Science
Meanshift and Camshift
In this chapter, Meanshift The intuition behind the meanshift is simple. Consider you have a set of points. (It can be a pixel distribution like histogram backprojection). You are given a small window...
Read more at OpenCV Tutorial
Mean Shift and Cam Shift Object Tracking
Among the most demanded features of computer vision is object tracking. Unfortunately, this is also one of the most difficult aspects of computer vision to implement. Not only does an object need to…
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A demo of the mean-shift clustering algorithm
A demo of the mean-shift clustering algorithm Reference: Dorin Comaniciu and Peter Meer, “Mean Shift: A robust approach toward feature space analysis”. IEEE Transactions on Pattern Analysis and Machin...
Read more at Scikit-learn Examples
Understanding Dataset Shift
Dataset shifting is one of those topics which is simple, perhaps so simple that it is considered trivially obvious. In my own data science classes the idea was discussed briefly, however, I think a…
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Fully Explained Mean Shift Clustering with Python
In this article, we cover the unsupervised learning algorithm in machine learning i.e. mean shift or mode-seeking algorithm. This clustering on the centroid-based algorithm in which the centroid…
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Mean Shift Clustering Algorithm Example In Python
Mean Shift is a hierarchical clustering algorithm. In contrast to supervised machine learning algorithms, clustering attempts to group…
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The Shift Operators and
The Java language provides three operator for performing bitwise shifting on 32 and 64 bit integer values. These are all binary operators with the first operand being the value to be shifted, and the ...
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Understanding Mean Shift Clustering and Implementation with Python
In this post, I briefly go over the concept of an unsupervised learning method, mean shift clustering, and its implementation in Python Continue reading on Towards Data Science
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Computer Vision — Mean-Shift Tracking
Previously, we have talked about a few algorithms on image segmentation and how we can find the object boundaries in an image. Let’s take a look at an algorithm that tracks objects whose appearance…
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Speeding up your code (1): the example of the mean shift clustering in Poincaré ball space
This is the first of a series of posts where I will describe the steps I did to build a fast clustering algorithm to be used within a particular, “hyperbolic” space using Python and Numpy. Because I…
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How to Detect Multivariate Covariate Shift in Machine Learning Models?
Yesterday’s post on covariate shift was appreciated by many of you. If you are new here or you somehow missed reading it, please read that post before proceeding ahead: Covariate Shift Is Way More Pro...
Read more at Daily Dose of Data Science
Image Shifting using NumPy from Scratch
Image shifting is simply shifting each pixel of the image to a new position. This is a method of pixel shift used in digital cameras to produce super-resolution images. We can think of a pixel as a…
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Mastering the shifts with variational autoencoders
However, now we can clearly formulate our expectations for the “ideal” unsupervised ML method for such data sets. It should be able to find the descriptors or disentangle the representations of the 1D...
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How to Interpret Reconstruction Loss While Detecting Multivariate Covariate Shift?
Today’s post is the last one in our three-part multivariate covariate shift detection series. Read the first two posts here if you missed them: Part 1: Covariate Shift Is Way More Problematic Than Mos...
Read more at Daily Dose of Data Science
Detecting Covariate Shift: A Guide to the Multivariate Approach
Good old PCA can alert you when the distribution of your production data changes Continue reading on Towards Data Science
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Making Convolutional Networks Shift-Invariant Again
What’s wrong with modern convolutional networks and how can we fix them? An overview, discussion and application of the recent April 2019 paper.
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Practical Issues in Data Science Part 2: Distribution Shift (Part 2)
In Distribution Shift (Part 1), I have briefly introduced 3 types of distribution shifts and focused on the methods to tackle covariate shift and prior probability shift. However, the methods…
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Covariate Shift Is Way More Problematic Than Most People Think
Almost all real-world ML models gradually degrade in performance due to covariate shift. For starters, covariate shift happens when the distribution of features changes over time, but the true (natura...
Read more at Daily Dose of Data Science
Time Series Analysis: Resampling, Shifting and Rolling
There are many definitions of time series data, all of which indicate the same meaning in a different way. A straightforward definition is that time series data includes data points attached to…
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Beware of Data Shifts
Even AIs built by some of the world's leading experts often struggle to replicate the promising performances outside of the “laboratory”. A prominent example is the Health Care AI systems developed…
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Signed vs unsigned shift
In Java, all number primitives are signed. For example, an int always represent values from [-2^31 - 1, 2^31], keeping the first bit to sign the value - 1 for negative value, 0 for positive. Basic shi...
Read more at Essential Java
torch.bitwise_left_shift
Computes the left arithmetic shift of input by other bits. The input tensor must be of integral type. This operator supports broadcasting to a common shape and type promotion . The operation applied i...
Read more at PyTorch documentation
Mean, Median, and Mode
In Machine Learning (and in mathematics) there are often three values that interests us: Mean, Median and Mode. Mean — It is the average value of the dataset. At its basic level, to calculate the…
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