Data Science

Showing 1-9 of 27 results
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Machine Learning Number Recognition - From Zero to Application

by Teimur Gasanov

Harnessing the potential of machine learning for computer vision is not a new concept but recent advances and the availability of new tools and datasets have made it more accessible to developers. In this article, Toptal Software Developer Teimur Gasanov demonstrates how you can create an app capable of identifying handwritten digits in under 30 minutes, including the API and UI.

10 minute readContinue Reading
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Embeddings in Machine Learning: Making Complex Data Simple

by Yaroslav Kopotilov

Working with non-numerical data can be challenging, even for seasoned data scientists. To make good use of such data, it needs to be transformed. But how? In this article, Toptal Data Scientist Yaroslav Kopotilov will introduce you to embeddings and demonstrate how they can be used to visualize complex data and make it usable.

11 minute readContinue Reading
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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
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Do the Math: Scaling Microservices Applications With Orchestrators

by Antoine Hamon

Automatically scaling container deployments in a microservices-based app architecture is downright luxurious...once it's set up. But what's the best way to tune an app's orchestration parameters?

9 minute readContinue Reading
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Three Principles of Data Warehouse Development

by Chamitha Wanaguru

Data warehouses aren’t exactly a new concept, but industry demand for data science services, coupled with the rise of AI and machine learning, is making them more relevant than ever. In this post, Toptal Data Warehouse Developer Chamitha Wanaguru outlines three basic principles you need to keep in mind when developing a new data warehouse.

9 minute readContinue Reading
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Developing a Bioinformatics Database for Disulfide Bonds Research

by Viktor Bojović

The Protein Data Bank (PDB) bioinformatics database is the world's largest repository of experimentally-determined structures of proteins, nucleic acids, and complex assemblies. All data is gathered using experimental methods such as X-ray, spectroscopy, crystallography, NMR, etc. This article explains how to extract, filter, and clean data from the PDB to make it suitable for further analysis.

25 minute readContinue Reading
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Introduction to Deep Learning Trading in Hedge Funds

by Neven Pičuljan

In this article, Toptal Freelance Software Engineer Neven Pičuljan introduces you to the intricacies of deep learning in hedge funds and finance in general.

21 minute readContinue Reading
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The Definitive Guide to NoSQL Databases

by Mohammad Altarade

Limited SQL scalability has prompted the industry to develop and deploy a number of NoSQL database management systems, with a focus on performance, reliability, and consistency. The trend was driven by proprietary NoSQL databases developed by Google and Amazon. Eventually, open-source systems like MongoDB, Cassandra, and Hypertable brought NoSQL within reach of everyone. In this post, Senior Software Engineer Mohamad Altarade dives into some of them and explains why NoSQL will probably be with us for years to come.

16 minute readContinue Reading
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A Data Engineer's Guide To Non-Traditional Data Storages

by Ken Hu

With the rise of big data and data science, storage and retrieval have become a critical pipeline component for data use and analysis. Recently, new data storage technologies have emerged. But the question is: Which one should you choose? Which one is best suited for data engineering? In this article, Toptal Data Scientist Ken Hu compares three prominent storage technologies within the context of data engineering.

7 minute readContinue Reading

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