Data Science and Databases

Showing 17-24 of 122 results

Getting the Most Out of Pre-trained Models

by Nauman Mustafa

Pre-trained models are making waves in the deep learning world. Using massive pre-training datasets, these NLP models bring previously unheard-of feats of AI within the reach of app developers.

10 minute readContinue Reading

Sound Logic and Monotonic AI Models

by Emmanuel Tsukerman

For those working with AI, the future is certainly exciting. At the same time, there is a general sense that AI suffers from one pesky flaw: AI in its current state can be unpredictably unreliable.

12 minute readContinue Reading

Flexible A/B Testing with AWS Lambda@Edge

by Georgios Boutsioukis

One of the new possibilities offered by Lambda@Edge is the ability to implement server-side A/B testing using Lambdas on CloudFront’s edge servers. In this article, Toptal Full-stack Developer Georgios Boutsioukis guides you through the process and outlines the pros and cons of A/B testing with Lambda@Edge.

9 minute readContinue Reading

Stars Realigned: Improving the IMDb Rating System

by Juan Manuel Ortiz de Zarate

IMDb ratings have genre bias: For example, dramas tend to score higher. Removing common feature bias and keeping unique characteristics, it's possible to create a new, refined score based on IMDb information.

10 minute readContinue Reading

Do Your Homework: 7 AWS Certified Solutions Architect Exam Tips

by Ross Bowman

Cloud architects with the AWS Certified Solutions Architect - Associate qualification are in high demand, with good reason—the AWS exam sets the bar high. What's the best way to prepare for it?

7 minute readContinue Reading

Timestamp Truncation: A Ruby on Rails ActiveRecord Tale

by Maciek Rząsa

Tests should keep apps from being flaky. But tests themselves can become flaky—even the most straightforward ones. Here's how we dove into a problematic test on a PostgreSQL-backed Rails app, and what we uncovered.

< 5 minute readContinue Reading

Semi-supervised Image Classification With Unlabeled Data

by Urwa Muaz

Supervised learning is the key to computer vision and deep learning. However, what happens when you don’t have access to large, human-labeled datasets? In this article, Toptal Computer Vision Developer Urwa Muaz demonstrates the potential of semi-supervised image classification using unlabeled datasets.

9 minute readContinue Reading

MCMC Methods: Metropolis-Hastings and Bayesian Inference

by Divyanshu Kalra

Markov Chain Monte Carlo (MCMC) methods let us compute samples from a distribution even though we can’t do this relying on traditional methods. In this article, Toptal Data Scientist Divyanshu Kalra will introduce you to Bayesian methods and Metropolis-Hastings, demonstrating their potential in the field of probabilistic programming.

12 minute readContinue Reading

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