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In a neural network, batch normalization is achieved through a normalization step that fixes the means and variances of each layer's inputs. Ideally, the normalization would be conducted over the entire training set, but to use this step jointly with stochastic optimization methods, it is impractical to use the global information.
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).
where is the batch size, is the height of the feature map, and is the width of the feature map. That is, even though there are only B {\displaystyle B} data points in a batch, all B H W {\displaystyle BHW} outputs from the kernel in this batch are treated equally.
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]
As of 2023, this mini-batch approach remains the norm for training neural networks, balancing the benefits of stochastic gradient descent with gradient descent. [ 14 ] By the 1980s, momentum had already been introduced, and was added to SGD optimization techniques in 1986. [ 15 ]
Universal approximation theorems are limit theorems: They simply state that for any and a criterion of closeness >, if there are enough neurons in a neural network, then there exists a neural network with that many neurons that does approximate to within . There is no guarantee that any finite size, say, 10000 neurons, is enough.
In machine learning, a neural network (also artificial neural network or neural net, abbreviated ANN or NN) is a model inspired by the structure and function of biological neural networks in animal brains. [1] [2] An ANN consists of connected units or nodes called artificial neurons, which loosely model the neurons in the brain. Artificial ...
The Chinchilla team recommends that the number of training tokens is twice for every model size doubling, meaning that using larger, higher-quality training datasets can lead to better results on downstream tasks. [5] [6] It has been used for the Flamingo vision-language model. [7]