A few examples of images from the Google-Landmarks dataset, including landmarks such as Big Ben, Sacre Coeur Basilica, the rock sculpture of Decebalus and the Megyeri Bridge, among others.
Source: googleblog.com
The computer scientist Barbara Engelhardt develops machine-learning models and methods to scour human genomes for the elusive causes and mechanisms of disease.
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Our new paper proposes a new learning paradigm called ‘Scheduled Auxiliary Control (SAC-X)’ which seeks to overcome the issue of exploration in control tasks. SAC-X is based on the idea that to learn complex tasks from scratch, an agent has to learn to explore and master a set of basic skills first. Just as a baby must develop coordination and balance before she crawls or walks—providing an agent with internal (auxiliary) goals corresponding to simple skills increases the chance it can understand and perform more complicated tasks.
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How does machine learning differ from traditional programming?What is loss, and how do I measure it?How does gradient descent work?How do I determine whether my model is effective?How do I represent my data so that a program can learn from it?How do I build a deep neural network?
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