Training is a critical step in any AI/ML project. Check out this roundup for some of the latest blogs and news articles on AI strategies, DRL training techniques, and data culture.

How to improve algorithm fairness and interpretability

Whether it’s explaining how a model works to a client or catching bias in the data, fairness and interpretability are critical to improving AI-powered decisions and insights. Here are a few techniques that can help improve your algorithms.

Applying the AI teacher/student strategy to simulations

What do you do when you hit a simulator bottleneck in Deep Reinforcement Learning training? In one of our projects, the answer was to apply the teacher/student strategy. Here’s how it works to accelerate your DRL training without breaking the bank.

Where are businesses leveraging Deep Reinforcement Learning?

DRL is moving from the lab to the manufacturing plant, the road, and more. Here are a few examples of how organizations are leveraging Deep Reinforcement Learning to solve complex business problems.

Neal India Hackathon (April 9-11)

We kick off our first-ever Neal Hackathon this weekend! The virtual event will be hosted by Neal India, with nine teams competing to create a working prototype of their project in 36 hours. Learn more about each team here.

You may also like…

• Data siloes stifle data culture. Here’s how data democratization plays a key role in data-driven organizations.
• AI models are getting faster, stronger and…bigger. This episode of the TWIMLAI podcast discusses the trend of larger models with David Carmona from Microsoft.
• The VisionOnEdge template can accelerate moving a Vision AI on the edge project to production.  Check out this demo to see how it works.