Free Courses
    В каталог

    Deep Learning

    3 423 курсов
    Attention Mechanisms
    Autoencoders
    Batch Normalization
    Bayesian Deep Learning
    Convolution
    Convolutional Networks
    Deep Networks
    Deep Neural Networks
    Deepfakes
    Equivariant Neural Networks
    Geometric Deep Learning
    Image Synthesis
    Keras
    Multimodal Generative Networks
    Neural Style Transfer
    Positional Encoding
    PyTorch
    ResNet
    Self-Attention
    Self-Attention Mechanisms
    Stable Diffusion
    StyleGANs
    TPUs
    Transformer Architecture
    Variational Autoencoders
    Machine Learning - Lecture 23
    UofU Data Science

    Machine Learning - Lecture 23

    Machine Learning Fundamentals - Lecture 26
    UofU Data Science

    Machine Learning Fundamentals - Lecture 26

    The Unreasonable Effectiveness of Mathematics in Large Scale Deep Learning - From Theory to Practice
    Centre for Networked Intelligence, IISc

    The Unreasonable Effectiveness of Mathematics in Large Scale Deep Learning - From Theory to Practice

    Floating Point Computations Today and in the Future
    Meeting Cpp

    Floating Point Computations Today and in the Future

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

    Solving the Many-Electron Schrödinger Equation with Deep Neural Networks

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

    Networks that Adapt to Intrinsic Dimensionality Beyond the Domain

    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

    Deep Neural Operators with Reliable Extrapolation for Multiphysics and Multiscale Problems
    Inside Livermore Lab

    Deep Neural Operators with Reliable Extrapolation for Multiphysics and Multiscale Problems

    DeepParticle: Learning Invariant Measure by Deep Neural Network Minimizing Wasserstein Distance
    Inside Livermore Lab

    DeepParticle: Learning Invariant Measure by Deep Neural Network Minimizing Wasserstein Distance

    AutoEncoders for Aerodynamic Predictions
    Inside Livermore Lab

    AutoEncoders for Aerodynamic Predictions

    Intrusive Model Order Reduction Using Neural Network Approximants
    Inside Livermore Lab

    Intrusive Model Order Reduction Using Neural Network Approximants

    Neural Differentiable Physics: Unifying Numerical PDEs and Deep Learning for Data-Augmented Computational Physics
    Inside Livermore Lab

    Neural Differentiable Physics: Unifying Numerical PDEs and Deep Learning for Data-Augmented Computational Physics

    Deep Dive into ChatGPT: Architecture and Comparison with Bard and LLaMa
    Applied Singularity

    Deep Dive into ChatGPT: Architecture and Comparison with Bard and LLaMa

    Multimodal Generative AI: Technology Overview and Business Implications
    Applied Singularity

    Multimodal Generative AI: Technology Overview and Business Implications

    Deploying Deep Learning Models for Inference at Production Scale
    Applied Singularity

    Deploying Deep Learning Models for Inference at Production Scale

    Introduction to Neural Networks
    Data Science Festival

    Introduction to Neural Networks

    How to Get Value Out of ChatGPT and Stable Diffusion Models with Proprietary Data
    Data Science Festival

    How to Get Value Out of ChatGPT and Stable Diffusion Models with Proprietary Data

    Advanced Python Libraries for Data Science
    Data Science Festival

    Advanced Python Libraries for Data Science

    Learning to Bound Using Decision Diagrams and Reinforcement Learning
    GERAD Research Center

    Learning to Bound Using Decision Diagrams and Reinforcement Learning