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    Machine Learning

    14 719 курсов
    Activation Functions
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    Amazon Comprehend
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    Automatic Differentiation
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    Bag of Words
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    Linear Discriminant Analysis
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    Physics Informed Machine Learning
    Physics Informed Neural Networks
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    Scientific Machine Learning
    Self-supervised Learning
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    Statistical Learning Theory
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    Supervised Learning
    Support Vector Machine (SVM)
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    Theoretical Machine Learning
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    Unsupervised Learning
    Unsupervised Machine Learning
    Variational Methods
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    Weakly Supervised Learning
    Word Embeddings
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    Zero-shot learning (ZSL)
    The Elusive Generalization: Classical Bounds to Double Descent to Grokking
    Simons Institute

    The Elusive Generalization: Classical Bounds to Double Descent to Grokking

    A Theory of Multi-objective Machine Learning
    Simons Institute

    A Theory of Multi-objective Machine Learning

    Understanding Generalization from Pre-training Loss to Downstream Tasks
    Simons Institute

    Understanding Generalization from Pre-training Loss to Downstream Tasks

    Prediction, Generalization, and Complexity in Statistical Decision Theory - Part 2
    Simons Institute

    Prediction, Generalization, and Complexity in Statistical Decision Theory - Part 2

    Practical Reinforcement Learning with Robots
    Montreal Robotics

    Practical Reinforcement Learning with Robots

    Differentiable Programming for Modeling and Control of Dynamical Systems
    Inside Livermore Lab

    Differentiable Programming for Modeling and Control of Dynamical Systems

    Solving the Many-Electron Schrödinger Equation with Deep Neural Networks
    Inside Livermore Lab

    Solving the Many-Electron Schrödinger Equation with Deep Neural Networks

    Scientific Uses of Automatic Differentiation - DDPS
    Inside Livermore Lab

    Scientific Uses of Automatic Differentiation - DDPS

    Networks that Adapt to Intrinsic Dimensionality Beyond the Domain
    Inside Livermore Lab

    Networks that Adapt to Intrinsic Dimensionality Beyond the Domain

    Competitive Physics Informed Networks - Overcoming Limitations in PDE Solutions
    Inside Livermore Lab

    Competitive Physics Informed Networks - Overcoming Limitations in PDE Solutions

    The Passive Symmetries of Machine Learning
    Inside Livermore Lab

    The Passive Symmetries of Machine Learning

    Bayesian Optimization: Exploiting Machine Learning Models, Physics, and Throughput Experiments
    Inside Livermore Lab

    Bayesian Optimization: Exploiting Machine Learning Models, Physics, and Throughput Experiments

    Using Data Science to Advance the Impact of Vascular Digital Twins in Medicine
    Inside Livermore Lab

    Using Data Science to Advance the Impact of Vascular Digital Twins in Medicine

    Big Data Inverse Problems - Promoting Sparsity and Learning to Regularize
    Inside Livermore Lab

    Big Data Inverse Problems - Promoting Sparsity and Learning to Regularize

    Scientific Machine Learning through the Lens of Physics-Informed Neural Networks
    Inside Livermore Lab

    Scientific Machine Learning through the Lens of Physics-Informed Neural Networks

    ML-driven Models for Material Microstructure and Mechanical Behavior - DDPS
    Inside Livermore Lab

    ML-driven Models for Material Microstructure and Mechanical Behavior - DDPS

    Adversarial Machine Learning: Categories, Concepts, and Current Landscape
    Inside Livermore Lab

    Adversarial Machine Learning: Categories, Concepts, and Current Landscape

    Scaling Up AI: Embedding Physics Modeling into End-to-end Learning and Harnessing Random Projection
    Inside Livermore Lab

    Scaling Up AI: Embedding Physics Modeling into End-to-end Learning and Harnessing Random Projection

    Data-Driven Modeling of Unknown Systems with Deep Neural Networks
    Inside Livermore Lab

    Data-Driven Modeling of Unknown Systems with Deep Neural Networks

    Physics-Enhanced Gaussian Processes for Learning of Electromechanical Systems
    Inside Livermore Lab

    Physics-Enhanced Gaussian Processes for Learning of Electromechanical Systems

    Data-Driven Multi-Scale Simulations for Materials-by-Design of Energetic Materials
    Inside Livermore Lab

    Data-Driven Multi-Scale Simulations for Materials-by-Design of Energetic Materials