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  2. Mamba (deep learning architecture) - Wikipedia

    en.wikipedia.org/wiki/Mamba_(deep_learning...

    Mamba [a] is a deep learning architecture focused on sequence modeling. It was developed by researchers from Carnegie Mellon University and Princeton University to address some limitations of transformer models , especially in processing long sequences.

  3. Flux (text-to-image model) - Wikipedia

    en.wikipedia.org/wiki/Flux_(text-to-image_model)

    Flux (also known as FLUX.1) is a text-to-image model developed by Black Forest Labs, based in Freiburg im Breisgau, Germany.Black Forest Labs were founded by former employees of Stability AI.

  4. Hyperparameter optimization - Wikipedia

    en.wikipedia.org/wiki/Hyperparameter_optimization

    In machine learning, hyperparameter optimization [1] or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process, which must be configured before the process starts. [2] [3]

  5. Hyperparameter (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Hyperparameter_(machine...

    In machine learning, a hyperparameter is a parameter that can be set in order to define any configurable part of a model's learning process. Hyperparameters can be classified as either model hyperparameters (such as the topology and size of a neural network) or algorithm hyperparameters (such as the learning rate and the batch size of an optimizer).

  6. Fine-tuning (deep learning) - Wikipedia

    en.wikipedia.org/wiki/Fine-tuning_(deep_learning)

    In deep learning, fine-tuning is an approach to transfer learning in which the parameters of a pre-trained neural network model are trained on new data. [1] Fine-tuning can be done on the entire neural network, or on only a subset of its layers, in which case the layers that are not being fine-tuned are "frozen" (i.e., not changed during backpropagation). [2]

  7. Neural scaling law - Wikipedia

    en.wikipedia.org/wiki/Neural_scaling_law

    In machine learning, a neural scaling law is an empirical scaling law that describes how neural network performance changes as key factors are scaled up or down. These factors typically include the number of parameters, training dataset size, [ 1 ] [ 2 ] and training cost.

  8. MindSpore - Wikipedia

    en.wikipedia.org/wiki/MindSpore

    On April 24, 2024, Huawei's MindSpore 2.3.RC1 was released to open source community with Foundation Model Training, Full-Stack Upgrade of Foundation Model Inference, Static Graph Optimization, IT Features and new MindSpore Elec MT (MindSpore-powered magnetotelluric) Intelligent Inversion Model.

  9. Data Version Control (software) - Wikipedia

    en.wikipedia.org/.../Data_Version_Control_(software)

    Created by ex-Microsoft data scientist Dmitry Petrov, DVC aimed to integrate the best existing software development practices into machine learning operations. [45] In 2018, [46] Dmitry Petrov together with Ivan Shcheklein, an engineer and entrepreneur, founded Iterative.ai, [4] [47] an MLOps company that continued the development of DVC ...