September 2022

Haskell in Production: NoRedInk

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In this edition of our Haskell in Production series, we feature NoRedInk – an EdTech product that helps students become better writers through its online, adaptive writing curriculum. The questions in the article were answered collaboratively by four representatives of NoRedInk – Blake Thomas, Christoph Hermann, Richard Feldmann, and Juliano Solanho. Read further to learn more about how they use Haskell at NoRedInk, why they decided to adopt it, and what is their approach to trying out new […]

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K-Nearest Neighbors Algorithm for ML

K-Nearest Neighbors Algorithm for ML

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The k-nearest neighbors (kNN) algorithm is a simple tool that can be used for a number of real-world problems in finance, healthcare, recommendation systems, and much more. This blog post will cover what kNN is, how it works, and how to implement it in machine learning projects. What is the k-nearest neighbors classifier? The k-nearest neighbors classifier (kNN) is a non-parametric supervised machine learning algorithm. It’s distance-based: it classifies objects based on their proximate neighbors’ classes. kNN is […]

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Functional Futures: Dependent Types with David Christiansen

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In this month’s episode of Functional Futures, our guest is David Christiansen, the Executive Director of the Haskell Foundation, a contributor to a number of dependently typed languages, and a dependent type advocate that has managed to introduce many people to the topic today through his work, talks, and texts. We cover topics such as dependent types, theorem proving, metaprogramming, and many more. We also discuss the book he co-authored with Daniel P. Friedman, The Little Typer, and […]

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Some Math behind Neural Tangent Kernel

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Neural networks are well known to be over-parameterized and can often easily fit data with near-zero training loss with decent generalization performance on test dataset. Although all these parameters are initialized at random, the optimization process can consistently lead to similarly good outcomes. And this is true even when the number of model parameters exceeds the number of training data points. Neural tangent kernel (NTK) (Jacot et al. 2018) is a kernel to explain the evolution of neural […]

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A Guide to One-Shot Learning

A Guide to One-Shot Learning

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Passport checks at airports, automatic turnstiles in office centers, signature verification – all these tasks require face or object recognition. Historically, deep learning algorithms use a lot of labeled training data for simple tasks like identifying objects in photos or recognizing faces. But in the above situations, we normally do not possess a large variety of photos for each person for training the AI. Therefore, we need an ML algorithm that would be able to perform recognition with […]

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