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How CapsNets can overcome some shortcomings of CNNs, including requiring less training data, preserving image details, and handling ambiguity. Capsule networks (CapsNets) are a hot new neural net architecture that may well have a profound impact on deep learning, in particular for computer vision. Wait, isn’t computer vision pretty much solved already? Haven’t we all seen fabulous examples of conv

Image from page 206 of "Mazes and labyrinths; a general account of their history and developments" (1922). (source: Internet Archive on Flickr) Reinforcement learning (RL) is about training agents to complete tasks. We typically think of this as being able to accomplish some goal. Take, for example, a robot we might want to train to open a door. Reinforcement learning can be used as a framework fo

Building and training your first TensorFlow graph from the ground up. The TensorFlow project is bigger than you might realize. The fact that it’s a library for deep learning, and its connection to Google, has helped TensorFlow attract a lot of attention. But beyond the hype, there are unique elements to the project that are worthy of closer inspection: The core library is suited to a broad family

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