InfraNodus: An Excellent Tool for Textual Data Analysis

Text mining relates to transforming unstructured text into a structured format to identify meaningful patterns and new insights. Companies can apply advanced analytical techniques and other deep learning algorithms to explore hidden relationships in their datasets. In this way, IBM defines the broad field of data science that works with the string data format: speech, … Read more

Hotspots: Automating Underperformance Regions Surfacing in Machine Learning Systems

Isolating Problematic Data for Remediation and Retraining ML models Photo by Clay Banks on Unsplash When a system has high dimensional data, troubleshooting the right data input regions becomes a difficult problem. Hotspots automates identifying regions associated with poor ML performance to significantly reduce time and error of finding such regions. We might have a … Read more

How the “Clusterbuster” Shiny App Helps Hundreds of Doctors and Epidemiologists Battle COVID-19 in the Netherlands

[This article was first published on RStudio Blog, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don’t. As 2020 closed, Eveline Geubbels, former COVID-19 Surveillance Coordinator at the Dutch … Read more

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Enhancing Software Peer Review with GitHub Automation

How to join this free online event with Arfon Smith, Karthik Ram, Maëlle Salmon and Noam Ross. Software peer-review involves coordinating and tracking many moving parts: software submissions, testing and diagnostics, assignment of editors and reviewers, and logging the progression of submissions through revisions and acceptance. On this call we will discuss how rOpenSci has … Read more

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One-N against the world!

[This article was first published on R on OSM, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don’t. We’re taking a short break from neural networks to return to … Read more

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Temporal Coils: Intro to Temporal Convolutional Networks for Time Series Forecasting in Python

A TCN Tutorial, Using the Darts Multi-Method Forecast Library Yesterday’s article offered a tutorial on recurrent neural networks (RNNs): their LSTM, GRU, and Vanilla variants. Today, let’s add Temporal Convolutional Networks (TCNs), as the tenth method in the fourth article of this little series on time series forecasters. Coil Slinky Spring , TheDigitalArtist, Pete Linforth— … Read more

Closing the AI Value Gap Part 2: AI Co-creation with Adaptive Analytics

In a previous post, I introduced the concept that AI is not going to achieve the massive financial impact that analysts have predicted. This is because the majority of the problems it is predicted to “solve” (via prescriptive analytics) are complex in nature, and therefore very difficult to solve using a static system. Techniques like … Read more

A Complete Guide of How to Choose and Apply the Right Statistical Test in Python

Get a basic understanding of when you should apply Z-test, T-test, Chi-Squared test, ANOVA, and Correlation Test Image by Author We often underestimate statistics and their importance and from my own experience, I can tell for certain that it’s the most important part of Data Science. One of the most useful parts of statistics in … Read more

Finding Patterns In Data Using NMF

Using Ecco for Non-Negative Metrics Factorization on Text Data NMF Visualization using EECO(Source: By Author) NLP-Natural Language Processing is one of the hottest topics in the field of Artificial Intelligence. It helps in building applications like chatbots, voice assistants, sentiment analysis, recommendation engines, etc. It is a budding field where most related companies are investing … Read more

Evaluating Multi-label Classifiers

– Accuracy- The Confusion Matrix- A multi-label classification example- Multilabel classification confusion matrix- Aggregate metrics- Some Common Scenarios Classification is an important application of machine learning, which involves applying algorithms that learn from data. Classification is a predictive modelling task that requires assigning a class label to a data point, and we can say that … Read more

Improving Your Data Visualizations with Stacked Bar Charts in Python

First, let’s generate some sample data to use in our data analysis and visualizations. To do so, run the following code below. expense_data = {“Person”: random.choices([“A”, “B”], k=30),”Amount”: random.sample(range(100, 200), 10) + random.sample(range(0, 99), 10) + random.sample(range(49, 499), 10),”Category”: [“Groceries”] * 10 + [“Restaurant”] * 10 + [“Appliances”] * 10,”Date”: pd.to_datetime(pd.date_range(‘2020-01-01′,’2020-10-01′, freq=’MS’).tolist() * 3)}df = … Read more

Why Fortnite Became the Most Addicting Game In History

An analysis of players’ stats using AI and K-Nearest Neighbor models Photo By Mark Decile on Unsplash Children are becoming so addicted to Fortnite that it’s causing panic in parents. With over 250 million players worldwide, $200 million monthly revenue, it seems everyone is playing Fortnite these days. Fortnite is becoming more addictive than heroin, … Read more

Slack Alerts from a SQL query — on BigQuery

And as a result, our Slack alert will look like: Image by Author GCP Service Account First, we’ll have to create a Service Account with the permissions to run our system. Even though there are multiple solutions to handle this, let’s make it simple and create a single Service Account that will contain every permission … Read more

Amazon MemoryDB for Redis now supports AWS CloudFormation

Amazon MemoryDB for Redis now supports AWS CloudFormation, enabling you to manage MemoryDB resources using CloudFormation templates. Amazon MemoryDB for Redis is a Redis-compatible, durable, in-memory database service that delivers ultra-fast performance. AWS CloudFormation lets you model, provision, and manage AWS and third-party resources by treating infrastructure as code. CloudFormation makes it easier for you to … Read more

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3 On-the-Job Tips for Data Analysts

Simple steps for better analytics You took all the classes needed to become a data analyst. SQL? Check. Python? Check. You recently became a data analyst, or you are an aspiring analyst. No doubt, your technical skills will bring you far in the role, but how can you become an even stronger analyst? A year … Read more

At Northwell Health, data interoperability and AI saves time and livesAt Northwell Health, data interoperability and AI saves time and livesProduct Manager, Cloud Healthcare APIs GoogleDirector of Software Engineering and Architecture, Emerging Technology & Innovation, Northwell Health

Lung cancer is the leading cause of cancer death in the United States and like any cancer, early detection is crucial to survival. Screening at-risk populations is an important part of reducing mortality, and if concerning nodules are found on imaging, further testing may be required. Today, we’ll share how Northwell Health uses Google Cloud … Read more

Training a CNN to detect stop signs without any manually annotated images

Bonus game: Exactly one sentence in this article is deliberately false. Can you find it? A stop sign. Image by the author. Annotating a large image dataset is labor-intensive, and as a consequence, expensive. In some cases, only large corporations have the resources to build the dataset that will give their convolutional neural networks (CNN) … Read more

5 Most Reliable Functions You Should Know in Python Programming

The map() function applies a specified function on each item in an iterable. Its syntax itself is self-explanatory. map(function,iterable) For example, let’s make a function to return an input word in the upper case. And then map this function to each item in the list. The simplest example of map() function in Python | Image … Read more

Machine Learning vs. Deep Learning: What’s the difference?

Diving into the difference between the 2 largest data science methods Photo from Unsplash by thisisengineering Machine Learning and Deep Learning are often confused with one another because they both fall under the data science umbrella. While Machine Learning and Deep Learning share similarities, there are also key differences between them. Here we’ll briefly explain … Read more

How to Solve Data Science Business Case Interview Questions

The Ultimate Guide to Preparing Business Case Interview Questions as a Data Scientist Image created by the author on Canva Similar to product sense interview questions, business case interview questions in data science are asked to understand your thought process behind your solution. Even if your solution provides an accurate answer to the question, if … Read more

lambda.min, lambda.1se and Cross Validation in Lasso : Continuous Response

#========================================================# # Quantitative ALM, Financial Econometrics & Derivatives  # ML/DL using R, Python, Tensorflow by Sang-Heon Lee  # # https://kiandlee.blogspot.com #——————————————————–# # Cross Validation of Lasso : Gaussian Regression #========================================================# library(glmnet)  graphics.off()  # clear all graphs rm(list = ls()) # remove all files from your workspace set.seed(1234) #============================================ # data : x and y #============================================ data(QuickStartExample) # built-in data nfolds = 5 # number of folds #============================================ # cross validation by using cv.glmnet #============================================ cvfit – cv.glmnet(     x, y, family = “gaussian”,      type.measure = “mse” ,     nfolds = nfolds,     keep = TRUE  # returns foldid  ) # two lambda from cv.glmnet cvfit$lambda.min; cvfit$lambda.1se x11(); plot(cvfit) #============================================ # cross validation by hand #============================================ # get a vector of fold id used in cv.glmnet # to replicate the same result. # Therefore, this is subject to the change foldid – cvfit$foldid # from glmnet # candidate lambda range fit      – glmnet(x, y, family = “gaussian”) v.lambda – fit$lambda nla      – length(v.lambda)      m.mse – matrix(0, nrow = nfolds, ncol=nla) #——————————- # iteration over all folds #——————————- for (i in 1:nfolds) {     # training   fold : tr     # validation fold : va          ifd – which(foldid==i) # i-th fold     tr.x – x[–ifd,]; tr.y – y[–ifd]     va.x – x[ifd,];  va.y – y[ifd]          # estimation using training fold     fit – glmnet(tr.x, tr.y, family = “gaussian”,  … Read more

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Little useless-useful R functions – Interactive Voronoi diagram generator using R and x11()

[This article was first published on R – TomazTsql, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don’t. Yes. Finally, the Voronoi diagrams with the use of x11() function. … Read more

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{binance} Spot Trading: Market Orders

[This article was first published on R – datawookie, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don’t. Functionality for working with spot trades is now available in {binance}. … Read more

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Smooth flow maps and a new edge bundling algorithm

[This article was first published on schochastics, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don’t. This post introduces new features of edgebundle, an R package that implements several … Read more

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Scheduling Rmarkdown files on Windows – your foolproof guide

[This article was first published on Data by John, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don’t. You’ve written a great rmarkdown file, and now you want to … Read more

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