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Machine learningand data mining. 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 ...
TensorFlow serves as a core platform and library for machine learning. TensorFlow's APIs use Keras to allow users to make their own machine-learning models. [ 41] In addition to building and training their model, TensorFlow can also help load the data to train the model, and deploy it using TensorFlow Serving.
scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. [3] It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific ...
A traffic alert and collision avoidance system ( TCAS, pronounced / tiːkæs /; TEE-kas ), is an aircraft collision avoidance system designed to reduce the incidence of mid-air collision (MAC) between aircraft. It monitors the airspace around an aircraft for other aircraft equipped with a corresponding active transponder, independent of air ...
t. e. Machine learning ( ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data and thus perform tasks without explicit instructions. [ 1] Recently, artificial neural networks have been able to surpass many previous approaches ...
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. "Keras 3 is a full rewrite of Keras [and can be used] as a low-level cross-framework language to develop custom components such as layers ...
Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] [18] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.
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]