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Florin Andrei
Florin Andrei

116 Followers

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Published in

Towards Data Science

·Jun 29

Visualizing the True Extent of the Curse of Dimensionality

Using the Monte Carlo method to visualize the behavior of observations with very large numbers of features — Think of a dataset, made of some number of observations, each observation having N features. If you convert all features to a numeric representation, you could say that each observation is a point in an N-dimensional space. When N is low, the relationships between points are just what you would…

Machine Learning

10 min read

Visualizing the full extent of the curse of dimensionality
Visualizing the full extent of the curse of dimensionality
Machine Learning

10 min read


Published in

Towards Data Science

·May 5

Train Image Segmentation Models to Accept User Feedback via Voronoi Tiling, Part 2

How to train an off-the-shelf image segmentation model to respond to user feedback — This is Part 2 of a series of articles about training image segmentation models so that the models respond to user feedback and adjust their predictions based on the feedback (mouse clicks). In Part 1 we’ve described the general strategy for training off-the-shelf image segmentation models to respond to user…

Image Segmentation

9 min read

Train Image Segmentation Models to Accept User Feedback via Voronoi Tiling, Part 2
Train Image Segmentation Models to Accept User Feedback via Voronoi Tiling, Part 2
Image Segmentation

9 min read


Published in

Towards Data Science

·May 5

Train Image Segmentation Models to Accept User Feedback via Voronoi Tiling, Part 1

How to train an off-the-shelf image segmentation model to respond to user feedback — Image segmentation is a popular topic in machine learning, with many practical applications. Vision models can be trained to partition images based on some criteria, usually following the contour of a familiar type of object. When the model can not only segment an image, but also distinguish between different types…

Image Segmentation

13 min read

Train Image Segmentation Models to Accept User Feedback via Voronoi Tiling, Part 1
Train Image Segmentation Models to Accept User Feedback via Voronoi Tiling, Part 1
Image Segmentation

13 min read


Published in

Towards Data Science

·Apr 10

Using Quantum Annealing for Feature Selection in scikit-learn

Feature selection for scikit-learn models, for datasets with many features, using quantum processing — Feature selection is a vast topic in machine learning. When done correctly, it can help reduce overfitting, increase interpretability, reduce the computational burden, etc. Numerous techniques are used to perform feature selection. …

Scikit Learn

11 min read

Using quantum annealing for feature selection in scikit-learn
Using quantum annealing for feature selection in scikit-learn
Scikit Learn

11 min read


Published in

Towards Data Science

·Jan 16

Quantum Computing for Optimization Problems — Solving the Knapsack Problem

How to solve an optimization problem using quantum computing, compared to a traditional solution — There are many problems that require finding the maximum or minimum of a function, called the objective function, that depends on several (or many) variables; certain constraints may or may not need to be applied to the variables. The variables could be binary, integer, elements in sets, floating point, etc…

Data Science

12 min read

Quantum Computing for Optimization Problems — Solving the Knapsack Problem
Quantum Computing for Optimization Problems — Solving the Knapsack Problem
Data Science

12 min read


Published in

Towards Data Science

·Feb 26, 2022

Use simulations to optimize customer wait time, systems load, and cost

Let’s say your infrastructure is a fleet of M identical machines, each processing a single task at a time. Each new customer is placed in a queue waiting for a machine to become idle. You know how long each processing task takes to complete, but it’s not a fixed number…

Data Science

13 min read

Use simulations to optimize customer wait time, systems load, and cost
Use simulations to optimize customer wait time, systems load, and cost
Data Science

13 min read


Published in

Towards Data Science

·Aug 29, 2021

Data has many periodic components you need to visualize? Treat it like audio!

Visualize periodic components using the Fourier transform. — You’re working with timeseries data, and you’re at the initial discovery stage. You’re trying to figure out all the different ways that the data varies in time. There may be trends, whereby the data is constantly increasing or decreasing in time. There may be completely random variations. …

Fourier Transform

7 min read

Data has many periodic components you need to visualize? Treat it like audio!
Data has many periodic components you need to visualize? Treat it like audio!
Fourier Transform

7 min read


Published in

Towards Data Science

·Jul 15, 2021

Backpropagation from Scratch: How Neural Networks Really Work

How do neural networks really work? I will show you a complete example, written from scratch in Python, with all the math you need to completely understand the process. I will explain everything in plain English as well. You could just follow along, read just the text and still get…

Neural Networks

16 min read

Backpropagation from scratch: how neural networks really work
Backpropagation from scratch: how neural networks really work
Neural Networks

16 min read


Published in

Towards Data Science

·Jan 14, 2021

Bootstrap statistics — how to work around limitations of simple statistical tests

You have a data sample. From it, you want to calculate a confidence interval for the population mean value. What’s the first thing you think about? It’s usually a t-test. But the t-test has several requirements, one of which is that the sampling distribution of the mean is nearly normal…

Data Science

7 min read

Bootstrap statistics — how to work around limitations of simple statistical tests
Bootstrap statistics — how to work around limitations of simple statistical tests
Data Science

7 min read


Published in

Towards Data Science

·Dec 28, 2020

Pfizer and Moderna — vaccine efficacy calculated from data

As of right now (December 2020), the leading mRNA vaccines for COVID-19 are made by Pfizer-BioNTech and Moderna. Their efficacy is estimated to be most likely around 94% … 95%. — But how was that number calculated? Turns out, the basic value is pretty easy. I’ll show you how to do that, and then I will make an estimate for how confident we are that the value is right. Randomized trials To test a vaccine, you need to do a randomized blind trial…

Statistics

6 min read

Pfizer and Moderna — vaccine efficacy calculated from data
Pfizer and Moderna — vaccine efficacy calculated from data
Statistics

6 min read

Florin Andrei

Florin Andrei

116 Followers

BS in Physics. MS in Data Science. Over a decade experience with cloud computing.

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