Between 30 April and 03 May, hundreds of researchers will gather in Vancouver, Canada, for the Sixth International Conference on Learning Representations. Here you will find details of all DeepMind’s accepted papers.
Source: deepmind.com
While simple, this thought experiment highlights some important aspects of human intelligence. For some tasks, we use a trial-and-error approach, and for others we use a planning approach. A similar phenomena seems to have emerged in reinforcement learning (RL).
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The paper attempts to predict whether crimes are gang-related using a neural network, a complex computational system modeled after a human brain that “learns” to classify or identify items based on ingesting a training dataset. The authors selected what they determined to be the four most important features (number of suspects, primary weapon used, the type of premises where the crime took place, and the narrative description of the crime) for identifying a gang-related crime from 2014–16 LAPD data and cross-referenced the crime incidents with a 2009 LAPD map of gang territory to create a training dataset for their neural network.
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From Kaggle to classrooms, one of the first lessons in machine learning involves decision trees. The reason for the focus on decision trees is that they aren’t very mathematics heavy compared to other ML approaches, and at the same time, they provide reasonable accuracy on classification problems.
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