This week, how algorithms are made.
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Ever wondered how big is Big Data? This article tries to draw an up-to-date comparison of the data generated by some of the most renowned data producers.
We are witnessing an ever-increasing production of digital data, so much to earn our epoch the title of Big Data era. Multiple and diverse players contribute to this growth, ranging from tech companies to standard industries, media agencies, institutions and research labs. Moreover, even everyday objects can collect data nowadays, thus including also ordinary people among data producers.
Deep neural networks have a huge advantage: They replace “feature engineering”—a difficult and arduous part of the classic machine learning cycle—with an end-to-end process that automatically learns to extract features.
However, finding the right deep learning architecture for your application can be challenging. There are numerous ways you can structure and configure a neural network, using different layer types and sizes, activation functions, and operations. Each architecture has its strengths and weaknesses. And depending on the application and environment in which you want to deploy your neural networks, you might have special requirements, such as memory and computational constraints.
Article by @TDataScience
Connect the Dots in Data Strategy
Data system should be a “digital twin” of the business processes
A few years ago, I worked in a new business in a public company. At the end of the first year, I worked on the annual review with the cofounder.
Everything seemed great until the financial forecast showed the new business would not generate the results as management expected from the very beginning. It turned out before the annual review, nobody had put all the business data in one place and interpreted what it meant. In the subsequent year, the company made a restructuring in the new business based on the insights from the annual review.
Article by @danfiehn
A significant milestone for a growing AI Fintech.
Incited is delighted to announce the opening of our brand-new UK office.
We are looking forward to welcoming our fantastic team as we embark on the next stage of our exciting journey.
If you’d like to hear more about our future or would like to come and join us, drop us a message – email@example.com.
Article by @Datasciencectrl
Application Integration vs Data Integration: A Comparison
Two very different approaches to data accessibility
Application integration and data integration are approaches taken by organizations to utilize data from different systems, but they meet different needs. They are the two of the most talked-about concepts in the world of IT and data, essentially involving data management. Yet, the concepts of application integration and data integration are deeply different, especially when it comes to how they are used and for what they are used.
And, while they are often incorrectly treated as the same both have the same goal — to make data more accessible and functional for the end-user. Now, as more and more companies across the broad spectrum of industries kick off their digital transformation journeys, confusion about the choice between data integration and app integration is bound to arise.
Article by @thenextweb
Yet, most social studies of algorithms perceive them as obscure black boxes that function autonomously. This isolated look at algorithms, which separates them from their human elements leads us to the wrong understanding and conclusions.
The Constitution of Algorithms, a book by Florian Jaton, Postdoctoral Researcher at the STS Lab at the University of Lausanne, sheds light on the human side of algorithms by exploring them from the inside instead of studying them from afar. Instead of working his way back from a working algorithm and trying to figure out how it came into being, Jaton starts from seemingly unrelated entities, such as people, desires, documents, curiosities, and then studies how all of these come together and interact to form what we call algorithms.