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  2. SqueezeNet - Wikipedia

    en.wikipedia.org/wiki/SqueezeNet

    SqueezeNet was originally described in SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size. [1] AlexNet is a deep neural network that has 240 MB of parameters, and SqueezeNet has just 5 MB of parameters. This small model size can more easily fit into computer memory and can more easily be transmitted over a ...

  3. PyTorch - Wikipedia

    en.wikipedia.org/wiki/PyTorch

    In September 2022, Meta announced that PyTorch would be governed by the independent PyTorch Foundation, a newly created subsidiary of the Linux Foundation. [ 24 ] PyTorch 2.0 was released on 15 March 2023, introducing TorchDynamo , a Python-level compiler that makes code run up to 2x faster, along with significant improvements in training and ...

  4. PyTorch Lightning - Wikipedia

    en.wikipedia.org/wiki/PyTorch_Lightning

    PyTorch Lightning is an open-source Python library that provides a high-level interface for PyTorch, a popular deep learning framework. [1] It is a lightweight and high-performance framework that organizes PyTorch code to decouple research from engineering, thus making deep learning experiments easier to read and reproduce.

  5. Training, validation, and test data sets - Wikipedia

    en.wikipedia.org/wiki/Training,_validation,_and...

    A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]

  6. Graph neural network - Wikipedia

    en.wikipedia.org/wiki/Graph_neural_network

    where is the matrix of node representations , is the matrix of node features , () is an activation function (e.g., ReLU), ~ is the graph adjacency matrix with the addition of self-loops, ~ is the graph degree matrix with the addition of self-loops, and is a matrix of trainable parameters.

  7. Lasso (statistics) - Wikipedia

    en.wikipedia.org/wiki/Lasso_(statistics)

    In statistics and machine learning, lasso (least absolute shrinkage and selection operator; also Lasso or LASSO) is a regression analysis method that performs both variable selection and regularization in order to enhance the prediction accuracy and interpretability of the resulting statistical model.

  8. Is it possible to retire comfortably on Social Security alone ...

    www.aol.com/possible-retire-comfortably-social...

    Their combined Social Security checks total $4,500 a month. ... The average benefit check is about $1,900. Suzanne, right, and Susie Leedy pose for a portrait at their home in McGaheysville, Va ...

  9. Neural scaling law - Wikipedia

    en.wikipedia.org/wiki/Neural_scaling_law

    Each of these variables can be defined as a real number, usually written as ,,, (respectively: parameter count, dataset size, computing cost, and loss). A neural scaling law is a theoretical or empirical statistical law between these parameters. There are also other parameters with other scaling laws.