Machine learning uses algorithms to turn a data set into a model that can identify patterns or make predictions from new data. Which algorithm works best depends on the problem.

The main reason for that is that debugging machine learning decisions or AI decisions, if you want, if you like, is incredibly hard, especially when you have … multiple layers of neural networks.

ADELPHI, Md.-- Army researchers discovered a way to quickly get information to Soldiers in combat using new machine learning techniques. The algorithms will play a significant role in enhancing ...

Reinforcement machine learning Chess would be an excellent example of this type of algorithm. The program knows the rules of the game and how to play, and goes through the steps to complete the round.

Deep learning is a subcategory of machine learning algorithms that use multi-layered neural networks to learn complex relationships between inputs and outputs. The more layers in the neural ...

Machine learning algorithms face two main constraints: Memory and processing speed. Let’s talk about memory first, which is usually the most limiting constraint. A modern PC typically has ...

The main difference is that unsupervised learning algorithms start with raw data, while supervised learning algorithms have additional columns or fields that are created by humans.

Machine learning is hard.Algorithms in a particular use case often either don't work or don't work well enough, leading to some serious debugging. And finding the perfect algorithm–the set of ...

Systems controlled by next-generation computing algorithms could give rise to better and more efficient machine learning products, a new study suggests.