Author: Savaram Ravindra Introduction The machine learning systems have been around since the 50s, but there are three major factors at play. They are improved algorithms, more powerful computer hardware, and the enormous increase of data. This is the reason why machine learning has gained a lot of significance than it had in the 50s. […]
Data Science Report
See how Mount Sinai is developing large-scale image data in neuropathology for building and evaluating deep learning algorithms to diagnose diseases.
Check out this informative guide on deep learning.
Take a look at how governments and enterprises are developing an ethical framework to ensure that AI continues to lead to the best decisions, without unintended consequences or misuse of data and analytics.
Take a look at how AI is revolutionizing the future for the following industries: Agriculture, Photography and Design, Healthcare, and Construction and City Planning.
Learn how a software engineer transitioned into machine learning through a data science bootcamp.
Google launches AI Platform to offer developers and data scientists an end-to-end service for building, testing and deploying their own models.
See how AI software is finding its way into medical robotics and how it’s being used by the healthcare industry.
Check out what is in store for data science in 2019. This article isn’t about new fads and concepts, but rather, dark horses – the trends that no one has thought about but will completely disrupt the working IT environment (for both good and bad – depends upon which side of the disruption you are on), in a significant manner.
See how one of the worlds biggest fragrance makers incorporated AI to suggest new formulations for particular markets. The technology gives them a head start on creating something novel, yet took nearly two years — and it required investments that still will take a while to recoup.