This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
While AI exists to simplify and/or accelerate decision-making or workflows, the methodology for doing so is often extremely complex. Indeed, some “black box” machine learning algorithms are so intricate and multifaceted that they can defy simple explanation, even by the computerscientists who created them.
Machine learning works on a known problem with tools and techniques, creating algorithms that let a machine learn from data through experience and with minimal human intervention. This led to the theory and development of AI. IBM computerscientist Arthur Samuel coined the phrase “machine learning” in 1952.
The advancement of computing power over recent decades has led to an explosion of digital data, from traffic cameras monitoring commuter habits to smart refrigerators revealing how and when the average family eats. Both computerscientists and business leaders have taken note of the potential of the data. What is MLOps?
r/compsci Anyone interested in sharing and discussing information that computerscientists find fascinating should visit the r/compsci subreddit. This contains a lot of posts about AI. r/AIethics Ethics are fundamental in AI. r/AIethics has the latest content on how one can use and create various AI tools ethically.
In this article, we present 7 key applications of computer vision in finance: No.1: 4: Algorithmic Trading and Market Analysis No.5: Privacy-preserving Computer Vision with TensorFlow Lite Other significant contributions include works by Andrew Ng. Applications of Computer Vision in Finance No.
We organize all of the trending information in your field so you don't have to. Join 15,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content