We help startups and enterprises to implement data science initiatives.
Learn the most important topics in the data science industry with our publications.
How can we get use of Data Science in everyday like? What impact can it make for administration tasks? Is that possible to use Data Science in Fraud detection or Robotization?
https://t.co/Gvy3Uuv5Cf
TensorFlow 2.1.0 was released a few days ago! The installation package now includes both the CPU and GPU versions, and it does not require a separate installation.
https://t.co/cLgirHKjGH
Kafka is a popular open-source stream processing platform. Whether you are an expert in Kafka or don’t know anything about it, you can find something useful in this list of tutorials for yourself.
https://t.co/2nDij71fnc
Green or renewable energy is one of the main trends of the modern world, as well as machine learning. If you want to learn about implementations of machine learning in the field and the future of ML with green energy, check out this article.
https://t.co/No3KQNg0wS
Deep-learning systems are truly amazing pieces of technology that emulates real humans better every year. This article focuses on a particular AI system that can mimic a human voice.
https://t.co/lD38p2Fr0r
It may be challenging to manage a vast amount of information without special instruments. If we are talking about users, we have a lot of questions we want to answer.
https://t.co/QE6XAe8NQ6
Polynote is a new polyglot notebook in open-source space, used by the Netflix team. Taking into account the position of the cell during execution, IDE functionality, autocomplete the ability to write each cell in a different language.
https://t.co/GWuWnIn5tO
New release of OpenAI’s GPT-2 is out. To find out more about this world-famous text generation model and finding its creators, check this article.
https://t.co/gSQlmLXmbR
Updates to TensorFlow are out! Now the topical version of TensorFlow is 2.1.0-rc0, and it supports Python 2. It also includes default support for CPU/GPU in one package using "pip install tensorflow."
https://t.co/3kBXFpxPQ5
Today, many complex AI tasks are usually solved with the help of Deep Learning. But often there are problems and related questions that arise.
https://t.co/5UTfdERgTq
Majority of the recent deep learning algorithms rely heavily on hand-labeled data. And while a lot of them were successful, hand-labeling is not always the best solution, as it is rather costly. Find out more about this and alternatives.
https://t.co/RNwbf2AxqA
AI-powered hiring system brought new challenges into the hiring process for prospect employees of some of America’s most prominent employers.
https://t.co/R85clIGuTG
Algorithm-based streaming services such as Spotify and TikTok are on the rise. Everything you see in your recommendations is derived either from your preferences or company policies.
https://t.co/9JRtMbFmVh
Thomas Edison is one of the most controversial inventors of his generation. Despite all debates about his legacy and innovations' uniqueness, there is one fundamental invention. Let's talk about it -->
https://t.co/gcd5chgNB7
The data scientist is a vague profession. The area of responsibility and the range of tools data scientists use are very different among subjects and organizations. In the article, the author tries to create a taxonomy of data scientists.
https://t.co/81c54OdRfn
Have you ever run a complex computational task? If so, you are probably aware of the constant convergence of cluster costs with service level agreements (SLAs). In terms of data and analytics, it is tough to cope with sudden surges in traffic.
https://t.co/9Mb17vMa7w
MongoDB and PostgreSQL are prevalent databases. But, don't confuse yourself, as they were created for different use cases. Here is an infographic that will help you to make the right decision.