Subject archive for "machine-learning," page 6

While humans have been using language since we arose, a complete understanding of language is a lifelong pursuit that often comes short, even for experts. To task computer technology with comprehending language, translating and even producing original written works represents a series of problems that are still in the process of being solved.

Phrases like deep learning and neural networks may be synonymous with artificial intelligence in the public’s minds, but for the data science teams that work with them, they are a breed of their own. Behind the dashboards of self-driving cars and below the online apps we use every day are a series of complex, interweaving algorithms that were inspired by the neurobiology of the human brain.

For data science teams working with inadequate data or too much data and not enough time or resources to process it, transfer learning can represent a significant shortcut in machine learning model development. Most often associated with deep learning and neural networks, it’s also being used in place of traditional machine learning model training techniques as a method of accelerating development.

In the last decade, machine learning (ML) models have made organizations all over the world and in every industry more productive, more profitable and better able to serve their clients.

Model validation is a core component of developing machine learning or artificial intelligence (ML/AI). While it’s separate from training and deployment, it should pervade the entire data science lifecycle.

Training a machine learning (ML) model is a process in which a machine learning algorithm is fed with training data from which it can learn. ML models can be trained to benefit businesses in numerous ways, by quickly processing huge volumes of data, identifying patterns, finding anomalies or testing correlations that would be difficult for a human to do unaided.