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ML.NET: Microsoft 2018 MIT license: Yes Windows, Linux, macOS C#, C++ C#, F# Yes Apache MXNet: Apache Software Foundation 2015 Apache 2.0: Yes Linux, macOS, Windows, [39] [40] AWS, Android, [41] iOS, JavaScript [42] Small C++ core library C++, Python, Julia, MATLAB, JavaScript, Go, R, Scala, Perl, Clojure: Yes No Yes No Yes [43] Yes [44] Yes ...
Transformer architecture is now used alongside many generative models that contribute to the ongoing AI boom. In language modelling, ELMo (2018) was a bi-directional LSTM that produces contextualized word embeddings, improving upon the line of research from bag of words and word2vec. It was followed by BERT (2018), an encoder-only Transformer ...
By 2019, graphics processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method for training large-scale commercial cloud AI . [169] OpenAI estimated the hardware computation used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017) and found a 300,000-fold increase in the ...
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]
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]
Multimodal learning is a type of deep learning that integrates and processes multiple types of data, referred to as modalities, such as text, audio, images, or video.This integration allows for a more holistic understanding of complex data, improving model performance in tasks like visual question answering, cross-modal retrieval, [1] text-to-image generation, [2] aesthetic ranking, [3] and ...
Machine learning in bioinformatics is the application of machine learning algorithms to bioinformatics, [1] including genomics, proteomics, microarrays, systems biology, evolution, and text mining.
Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] 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.