Deep learning vs. machine learning: what's the difference between the two? We provide a simplified explanation of both AI-based technologies.

Deep neural networks (DNNs), the machine learning algorithms underpinning the functioning of large language models (LLMs) and other artificial intelligence (AI) models, learn to make accurate ...

Machine Learning systems, also called models, are trained by humans to use an algorithm to classify and analyze data, make predictions, and take actions of limited complexity.

TensorFlow is a Python-friendly open source library for developing machine learning applications and neural networks. Here's what you need to know about TensorFlow.

I will start out by explaining what machine learning is, along with the different types of machine learning, and then I will jump into explaining common models.

Azure Machine Learning has both AutoML, which sweeps through features and algorithms, and hyperparameter tuning, which you typically run on the best algorithm chosen by AutoML.

Computer algorithms are a set of instructions used to calculate and solve a problem. The quality of AI deep learning depends not only on the algorithm but also on the data.

Usually, in supervised learning, training data is manually labeled by subject-matter domain experts to prepare it to train the AI algorithm—a time-consuming, laborious, and therefore costly task.

Machine learning may find things that humans would miss; furthermore, the more data that is fed to the algorithms, the better they get at identifying trends and patterns.