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Florin Andrei
Florin Andrei

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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


Oct 31, 2020

Test results, part 2: how about negative results?

This is part 2 of this article: Test results are positive. What are the odds you have the virus? Surprise, intuition is wrong! Bayes’s Theorem from statistics applied to pandemicsflorin-andrei.medium.com To recap: there’s a pandemic going on, 1% of people have the virus. There’s a test that can detect the virus, and the test is 99% reliable (for both positive and negative results). But this time, when you take the test, the result…

Statistics

2 min read

Test results, part 2: how about negative results?
Test results, part 2: how about negative results?
Statistics

2 min read


Oct 30, 2020

Test results are positive. What are the odds you have the virus? Surprise, intuition is wrong!

Let’s say there’s a virus pandemic sweeping through the population, and 1% of people have the virus. Let’s say there’s a test for this condition, and the test is 99% reliable, meaning — out of 100 tested cases, the test will be correct in 99 cases, and will be wrong…

Statistics

5 min read

Test results are positive. What are the odds you have the virus? Surprise, intuition is wrong!
Test results are positive. What are the odds you have the virus? Surprise, intuition is wrong!
Statistics

5 min read


Published in Towards Data Science

·Jun 21, 2020

Removing non-linear trends from timeseries data

Sometimes trends need to be removed from timeseries data, in preparation for the next steps, or part of the data cleaning process. If you can identify a trend, then simply subtract it from the data, and the result is detrended data. If the trend is linear, you can find it…

Regression

5 min read

Removing non-linear trends from timeseries data
Removing non-linear trends from timeseries data
Regression

5 min read


Published in Towards Data Science

·Jun 7, 2020

The potato train — using Python with extremely large numbers and arbitrary precision for binomial probability

Math is hard, let’s go shopping — for tutorials, that is. I definitely wish I had read this tutorial before trying some things in Python that involve extremely large numbers (binomial probability for large values of n) and my code started to crash. But wait, I hear you saying, Python…

Python

9 min read

The potato train — using Python with extremely large numbers and arbitrary precision for binomial…
The potato train — using Python with extremely large numbers and arbitrary precision for binomial…
Python

9 min read

Florin Andrei

Florin Andrei

83 Followers

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

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