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  1. Deep learning

    Deep learning (also known as deep structured learning or differential programming) is part of a broader family of machine learning methods based on artificial

  2. Deep reinforcement learning

    Deep reinforcement learning (DRL) uses deep learning and reinforcement learning principles to create efficient algorithms applied on areas like robotics

  3. Deeper learning

    In U.S. education, deeper learning is a set of student educational outcomes including acquisition of robust core academic content, higher-order thinking

  4. Comparison of deep-learning software

    compares notable software frameworks, libraries and computer programs for deep learning. Licenses here are a summary, and are not taken to be complete statements

  5. Q-learning

    Q-learning algorithm. In 2014 Google DeepMind patented an application of Q-learning to deep learning, titled "deep reinforcement learning" or "deep Q-learning"

  6. Machine learning

    hearing. Some successful applications of deep learning are computer vision and speech recognition. Decision tree learning uses a decision tree as a predictive

  7. Deepfake

    Deepfakes (a portmanteau of "deep learning" and "fake") are synthetic media in which a person in an existing image or video is replaced with someone else's

  8. Artificial neural network

    recognition problems, which became known as "deep learning".{{@book{Goodfellow-et-al-2016, title={Deep Learning}, author={Ian Goodfellow and Yoshua Bengio

  9. Deep Learning Studio

    Deep Learning Studio is a software tool that aims to simplify the creation of deep learning models used in artificial intelligence. It is compatible with

  10. Reinforcement learning

    Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs