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  2. Navy Marine Corps Intranet - Wikipedia

    en.wikipedia.org/wiki/Navy_Marine_Corps_Intranet

    Navy Marine Corps Intranet. The Navy/Marine Corps Intranet ( NMCI) is a United States Department of the Navy program which was designed to provide the vast majority of information technology services for the entire Department, including the United States Navy and Marine Corps .

  3. Boosting (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Boosting_(machine_learning)

    Machine learningand data mining. In machine learning, boosting is an ensemble meta-algorithm for primarily reducing bias, variance [1]. It is used in supervised learning and a family of machine learning algorithms that convert weak learners to strong ones. [2]

  4. Multimodal learning - Wikipedia

    en.wikipedia.org/wiki/Multimodal_learning

    t. e. Multimodal learning, in the context of machine learning, is a type of deep learning using a combination of various modalities of data, such as text, audio, or images, in order to create a more robust model of the real-world phenomena in question. In contrast, singular modal learning would analyze text (typically represented as feature ...

  5. Long short-term memory - Wikipedia

    en.wikipedia.org/wiki/Long_short-term_memory

    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 RNNs, hidden Markov models and other sequence learning methods. It aims to provide a short-term memory for RNN ...

  6. Timeline of machine learning - Wikipedia

    en.wikipedia.org/wiki/Timeline_of_machine_learning

    1950s. Pioneering machine learning research is conducted using simple algorithms. 1960s. Bayesian methods are introduced for probabilistic inference in machine learning. [1] 1970s. ' AI winter ' caused by pessimism about machine learning effectiveness. 1980s.

  7. Knowledge distillation - Wikipedia

    en.wikipedia.org/wiki/Knowledge_distillation

    Knowledge distillation. In machine learning, knowledge distillation or model distillation is the process of transferring knowledge from a large model to a smaller one. While large models (such as very deep neural networks or ensembles of many models) have higher knowledge capacity than small models, this capacity might not be fully utilized.

  8. Machine learning in bioinformatics - Wikipedia

    en.wikipedia.org/wiki/Machine_learning_in...

    t. e. Machine learning in bioinformatics is the application of machine learning algorithms to bioinformatics, [1] including genomics, proteomics, microarrays, systems biology, evolution, and text mining. [2] [3] Prior to the emergence of machine learning, bioinformatics algorithms had to be programmed by hand; for problems such as protein ...

  9. Transformer (deep learning architecture) - Wikipedia

    en.wikipedia.org/wiki/Transformer_network

    A transformer is a deep learning architecture developed by Google and based on the multi-head attention mechanism, proposed in a 2017 paper "Attention Is All You Need". [1] Text is converted to numerical representations called tokens, and each token is converted into a vector via looking up from a word embedding table. [1]