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With more board configurations than there are atoms in the universe, the ancient Chinese game of Go has long been considered a grand challenge for artificial intelligence. On March 9, 2016, the worlds of Go and artificial intelligence collided in South Korea for an extraordinary best-of-five-game competition, coined The DeepMind Challenge Match. Hundreds of millions of people around the world watched as a legendary Go master took on an unproven AI challenger for the first time in history.
Accurate models of the world are built on notions of its underlying symmetries. In physics, these symmetries correspond to conservation laws, such as for energy and momentum. But neural network models struggle to learn these symmetries. To address this shortcoming, last year I introduced a class of models called Hamiltonian Neural Networks (HNNs) that can learn these invariant quantities directly from (pixel) data. In this project, some friends and I are going to introduce a complimentary class of models called Lagrangian Neural Networks (LNNs). These models are able to learn Lagrangian functions straight from data. They’re cool because like HNNs they can learn exact conservation laws, but unlike HNNs they don’t require canonical coordinates.
In this tutorial, you will learn how to automatically detect COVID-19 in a hand-created X-ray image dataset using Keras, TensorFlow, and Deep Learning.
Linear Discriminant Analysis, or LDA, is a useful technique in machine learning for classification and dimensionality reduction. It's often used as a preprocessing step since a lot of algorithms perform better on a smaller number of dimensions.
The widespread success of deep learning across a range of domains such as natural language processing, conversational agents, and connectomics, has transformed the landscape of research in machine learning and left researchers with a number of interesting and important open questions such as: Why do deep neural networks (DNNs) generalize so well despite being overparameterized? What is the relationship between architecture, training, and performance for deep networks? How can one extract salient features from deep learning models?