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Keras is an open-source library that provides a Python interface for artificial neural networks. Keras was first independent software, then integrated into the TensorFlow library , and later supporting more.
Blue Brain Project, an attempt to create a synthetic brain by reverse-engineering the mammalian brain down to the molecular level. [1] Google Brain, a deep learning project part of Google X attempting to have intelligence similar or equal to human-level. [2] Human Brain Project, ten-year scientific research project, based on exascale ...
It disregards word order (and thus most of syntax or grammar) but captures multiplicity. The bag-of-words model is commonly used in methods of document classification where, for example, the (frequency of) occurrence of each word is used as a feature for training a classifier. [1] It has also been used for computer vision. [2]
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]
In software and systems engineering, a use case is a potential scenario in which a system receives an external request (such as user input) and responds to it. A use case is a list of actions or event steps typically defining the interactions between a role (known in the Unified Modeling Language (UML) as an actor) and a system to achieve a goal.
A set of books extracted from the Project Gutenberg books library Text Natural Language Processing 2019 Jack W et al. Deepmind Mathematics: Mathematical question and answer pairs. Text Natural Language Processing 2018 [115] D Saxton et al. Anna's Archive: A comprehensive archive of published books and papers None 100,356,641 Text, epub, PDF
A word n-gram language model is a purely statistical model of language. It has been superseded by recurrent neural network–based models, which have been superseded by large language models. [12] It is based on an assumption that the probability of the next word in a sequence depends only on a fixed size window of previous words.
For example, if each query is routed to one expert as in Switch Transformers, and if the experts are load-balanced, then each expert should expect on average / queries in a batch. In practice, the experts cannot expect perfect load balancing: in some batches, one expert might be underworked, while in other batches, it would be overworked.