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Featured Blog Posts – July 2020 Archive (13)

Traditional vs Deep Learning Algorithms used in BlockChain in Retail Industry

Use of SecureSVM, Boosting, Bagging, Clustering, LSTM, CNN, GAN in Retail with BlockChain

Introduction

This blog highlights different ML algorithms used in blockchain transactions with a special emphasis on bitcoins in retail payments. This blog is structured as follows:

  • Overview of the role of blockchain in the retail industry.
  • Different traditional…
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Added by Sharmistha Chatterjee on July 26, 2020 at 7:22am — No Comments

Anomaly Detection from Head and Abdominal Fetal ECG — A Case study of IOT anomaly detection using Generative Adversarial Networks

Anomaly Detection from Head and Abdominal Fetal ECG — A Case study of IOT anomaly detection using Generative Adversarial Networks

Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal…

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Added by Sharmistha Chatterjee on July 26, 2020 at 7:14am — No Comments

Why we need more Bayesian trained data scientists than frequentist post COVID 19 ..

Earlier this week, I was speaking at an event on AI for Real Estate where I showed an example from a BBC clip which said that “central London is now a ghost town” (due to COVID 19)

A few months ago, this headline would have been laughable

In London, central London and the London underground are a key fabric of daily…

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Added by ajit jaokar on July 22, 2020 at 2:00pm — No Comments

Overview on Forecasting Models in Power BI

Time Series forecasting in PBI is based on the thumb technique of smoothening time series prediction called Exponential Smoothening (ES). ES of time series data assigns exponentially decreasing weights for newest to oldest observations. ES is also be used for time series with trend and seasonality. This model is usually used to make short term forecasts, as longer-term forecasts using this technique can be quite unreliable. Collectively, the methods are sometimes referred to as ETS models,…

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Added by Vigneswaran S on July 19, 2020 at 1:01am — No Comments

Quantum Neural Net, an Oxymoron

oxymoron

Venn diagram showing overlap between "quantum" and "neural network"

The term "quantum neural networks" is being used with increasing frequency by the quantum computing community. Maybe as a dishonest, bait-and-switch advertising strategy, this makes sense. However, from a scientific standpoint, "quantum neural network" is a very poor name choice for what is being alluded to here.

Artificial Neural Networks, often called "Neural Nets" for short, are supposed to…

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Added by Robert R. Tucci on July 15, 2020 at 4:30am — No Comments

Overcoming an Imbalanced Dataset using Oversampling.

How oversampling yielded great…

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Added by Michael Burkhardt on July 10, 2020 at 3:30am — No Comments

Model Selection: Adjusted Coefficient of Determination-Variance Tradeoff

In my previous article, we analyzed the COVID-19 data of Turkey and selected the cubic model for predicting the spread of disease. In this article, we will show in detail why we selected the cubic model for prediction and see whether our decision was right or not.

When we analyze the regression trend models we should consider overfitting and underfitting…

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Added by Selcuk Disci on July 10, 2020 at 2:30am — No Comments

Simpson’s Paradox, the Bane of Clinical Trials

(This article is now a chapter of my github proto-book Bayesuvius)

Simpson's paradox is a recurring nightmare for all statisticians overseeing a clinical trial for a medicine. It is possible that if they leave out a certain "confounding" variable from a study, the study's conclusion on whether a medicine is effective or not, might be, without measuring that confounding variable, the opposite of what it would have been had that variable been measured. Statisticians have to enlist expert…

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Added by Robert R. Tucci on July 9, 2020 at 7:00pm — No Comments

How to Communicate Data

The following graphic is based on Sam Priddy's excellent DSC/Tableau Webinar How to Accelerate and Scale Your Data Science Workflows. Sam covered many interesting points for organizing, analyzing and presenting data--including which graph is best suited for different data types. This graphic is an overview of some of Sam's points. For more…

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Added by Stephanie Glen on July 8, 2020 at 9:02am — 1 Comment

Fourier Series and Differential Equations with some applications in R and Python (Part 2)

This is the 2nd part of the article on a few applications of Fourier Series in solving differential equations. All the problems are taken from the edx Course: MITx - 18.03Fx: Differential Equations Fourier Series and Partial Differential Equations. The article…

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Added by Sandipan Dey on July 4, 2020 at 5:00pm — No Comments

Fourier Series and Differential Equations with some applications in R (Part 1)

In this article, a few applications of Fourier Series in solving differential equations will be described. All the problems are taken from the edx Course: MITx - 18.03Fx: Differential Equations Fourier Series and Partial Differential Equations. The article will be posted in two parts (two separate blongs)

First a basic introduction to the Fourier series will be given and then we shall see how to solve the following ODEs / PDEs using Fourier series:

  1. Find…
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Added by Sandipan Dey on July 4, 2020 at 5:00pm — 3 Comments

Data As Commodity: For Data Science Professional

What is a commodity?

Commodity are basic raw materials with certain standards that are used with other goods, commodity are often the basis of the production of various finished goods or services and then referred to as commodities (something made based on commodity). Some examples of commodity are seen from their types: metal (gold, silver, platinum, and copper); energy (crude oil and natural gas); livestock and agriculture (beef, mutton, rice, wheat, corn, soybeans,…

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Added by Jeefri A. Moka on July 3, 2020 at 6:30pm — No Comments

Data Governance: Bottom Up Approach to break silos

Over a period, large organizations have been transformed into disintegrated silos that has grown to be a major impediment to respond to changing market demands with agility. Organizational silos have led to innumerous disconnected policies, systems, processes, standards, teams. In order to sustain in this digital era, it’s imperative for organizations to take a top down enterprise value chain view to continually evaluate its ability to adapt (as well as compete) to changing market…

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Added by Subin George on July 3, 2020 at 3:40am — No Comments

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