We are excited to share some of the best and most recent machine learning courses available on YouTube. There are many plans to keep improving this collection. For instance, I will be sharing notes and better organizing individual lectures in a way that provides a bit of guidance for those that are getting started with machine learning. If you are interested to contribute, feel free to open a PR with links to all individual lectures for each course. It will take a bit of time, but I have plans to do many things with these individual lectures.

After discussing in my last two articles how digital twins can revolutionize the energy industry and how our Heat Transfer Twin can help HRSG (heat recovery steam generator) operators save millions of dollars, today I'd like to take a closer look at how a Heat Transfer Twin could be developed. As already explained, conventional inspections of HRSG walls and tubes require considerable manual effort and take up to three weeks. To reduce this expense, it's important to know in advance where corrosion might have occurred. However, identifying corrosion risks is very complicated because corrosion depends on several factors. We need to know if and how much liquid is in the steam, and where it hits the tubes.

This Course 4,132 recent views Natural Creativity is about looking into the many theories behind creativity, examining studies, and exploring the latest machine learning techniques. You will experiment with artistic tools that are powered by the latest machine learning techniques and conduct a full iteration of design thinking research that can be applied in several different business scenarios. You'll focus on how the concept of creativity has evolved over time and how it has been investigated. Then, you will actively engage and collaborate with others in the class through discussion prompts and have an opportunity to reflectively write about a project that inspires your creativity. Throughout the course, you will step away from your computer, plan, and conduct one iteration of a design project applying the techniques and theories you just learned, enabling you to gain deep insights on how people are creative in your own local environment.

Are you aware that machine learning is impacting almost every next activity of your life! The way you search, read, communicate, express everything is being taken over by machine learning!And it is imperative to be aware about it before you give in to your creative mindset! So, machine learning is pretty much exploiting you by gathering your data, your liking and disliking but what would be cooler is to know being used and then use it to be used! Here we are going to talk 3 different ways where machine learning has gathered you with all force, so lets get started with top 3 machine learning examples and applications used in everyday life. Recommendation systems are now everywhere!

Machine learning is a field of artificial intelligence (AI) that allows a computer-controlled software or system to take decisions and learn based on the analysis of empirical data from a database or physical sensors. The ease of use of Vi TECHNOLOGY's 3D SPI is made possible thanks to machine learning. The Pi series 3D SPI inspection systems' revolutionary ergonomics and award-winning programming simplicity are made possible using patented machine learning algorithms. In addition, the color and therefore the shape and position of the screen printing are also learned during the programming phase. Unique algorithm measures exact height of paste deposits The R&D team at Vi TECHNOLOGY developed an algorithm allowing the system to learn how to locate the paste deposits, without relying only on the location patterns, which are often insufficient due to the stretch or warpage of the board.

The deep-learning models need to be shipped as part of the operating system, taking up valuable NAND storage space. They also need to be loaded into RAM and require significant computational time on the GPU and/or CPU. Unlike cloud-based services, whose resources can be dedicated solely to a vision problem, on-device computation must take place while sharing these system resources with other running applications. Finally, the computation must be efficient enough to process a large Photos library in a reasonably short amount of time, but without significant power usage or thermal increase.

Security system that look at past attacks to help deal with future ones are deeply flawed. Here's an alternative approach that uses the latest machine learning tech. It's no secret that there's a constant game of cat and mouse playing out between hackers and security experts, with both sides working their hardest to stay ahead of the other. While hackers and assorted cyberattackers are always on the lookout for new vulnerabilities to exploit, however, unfortunately security systems can be a bit backwards looking in their approach -- relying on digging back in the archives to try and see how future hacks may play out. That's what Antigena, a machine learning security system developed by British cybersecurity startup DarkTrace is trying to change.