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Attention is a machine learning method that determines the relative importance of each component in a sequence relative to the other components in that sequence. In natural language processing, importance is represented by "soft" weights assigned to each word in a sentence. More generally, attention encodes vectors called token embeddings ...
Slab allocation is a memory management mechanism intended for the efficient memory allocation of objects. In comparison with earlier mechanisms, it reduces fragmentation caused by allocations and deallocations. This technique is used for retaining allocated memory containing a data object of a certain type for reuse upon subsequent allocations ...
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]
The Long Short-Term Memory (LSTM) cell can process data sequentially and keep its hidden state through time. Long short-term memory (LSTM) [ 1 ] is a type of recurrent neural network (RNN) aimed at dealing with the vanishing gradient problem [ 2 ] present in traditional RNNs. Its relative insensitivity to gap length is its advantage over other ...
One model of a machine learning is producing a function, f(x), which given some information, x, predicts some variable, y, from training data and . It is distinct from mathematical optimization because f {\displaystyle f} should predict well for x {\displaystyle x} outside of X train {\displaystyle X_{\text{train}}} .
A linked list is a sequence of nodes that contain two fields: data (an integer value here as an example) and a link to the next node. The last node is linked to a terminator used to signify the end of the list. In computer science, a linked list is a linear collection of data elements whose order is not given by their physical placement in memory.
A standard Transformer architecture, showing on the left an encoder, and on the right a decoder. Note: it uses the pre-LN convention, which is different from the post-LN convention used in the original 2017 Transformer. A transformer is a deep learning architecture developed by researchers at Google and based on the multi-head attention ...
Vapnik–Chervonenkis dimension. In Vapnik–Chervonenkis theory, the Vapnik–Chervonenkis (VC) dimension is a measure of the size (capacity, complexity, expressive power, richness, or flexibility) of a class of sets. The notion can be extended to classes of binary functions. It is defined as the cardinality of the largest set of points that ...