Leaving out neural networks and deep learning, which require a much higher level of computing resources, the most common algorithms are Naive Bayes, Decision Tree, Logistic Regression, K-Nearest ...

Combining graphs and machine learning has been getting a lot of attention lately, especially since the work published by researchers from DeepMind, Google Brain, MIT, and the University of Edinburgh.

Of all the excellent machine learning and deep learning frameworks available, TensorFlow is the most mature, has the most citations in research papers (even excluding citations from Google ...

The key difference between ML and DL . One of the biggest differences between deep learning and other forms of machine learning is the level of “supervision” that a machine is provided.

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

The paper, "Relational inductive biases, deep learning, and graph networks," posted on the arXiv pre-print service, is authored by Peter W. Battaglia of Google's DeepMind unit, along with ...

Machine Learning Specialization. This specialization, created in collaboration with Stanford Online and DeepLearning.AI, is a three-course program covering supervised learning (linear regression ...