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  2. List of datasets for machine-learning research - Wikipedia

    en.wikipedia.org/wiki/List_of_datasets_for...

    Major advances in this field can result from advances in learning algorithms (such as deep learning), computer hardware, and, less-intuitively, the availability of high-quality training datasets. [1] High-quality labeled training datasets for supervised and semi-supervised machine learning algorithms are usually difficult and expensive to ...

  3. Transformer (deep learning architecture) - Wikipedia

    en.wikipedia.org/wiki/Transformer_(deep_learning...

    The transformer model has been implemented in standard deep learning frameworks such as TensorFlow and PyTorch. Transformers is a library produced by Hugging Face that supplies transformer-based architectures and pretrained models. [11]

  4. Foundation model - Wikipedia

    en.wikipedia.org/wiki/Foundation_model

    A foundation model, also known as large X model (LxM), is a machine learning or deep learning model that is trained on vast datasets so it can be applied across a wide range of use cases. [1] Generative AI applications like Large Language Models are often examples of foundation models. [1]

  5. Large language model - Wikipedia

    en.wikipedia.org/wiki/Large_language_model

    A large language model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation. LLMs are language models with many parameters, and are trained with self-supervised learning on a vast amount of text. The largest and most capable LLMs are generative pretrained transformers (GPTs).

  6. Generative pre-trained transformer - Wikipedia

    en.wikipedia.org/wiki/Generative_pre-trained...

    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.

  7. Deep learning - Wikipedia

    en.wikipedia.org/wiki/Deep_learning

    Deep models (CAP > two) are able to extract better features than shallow models and hence, extra layers help in learning the features effectively. Deep learning architectures can be constructed with a greedy layer-by-layer method. [11] Deep learning helps to disentangle these abstractions and pick out which features improve performance. [8]

  8. Comparison of deep learning software - Wikipedia

    en.wikipedia.org/wiki/Comparison_of_deep...

    Has pretrained models Recurrent nets Convolutional nets RBM/DBNs Parallel execution (multi node) Actively developed BigDL: Jason Dai (Intel) 2016 Apache 2.0: Yes Apache Spark Scala Scala, Python No No Yes Yes Yes Yes Caffe: Berkeley Vision and Learning Center 2013 BSD: Yes Linux, macOS, Windows [3] C++: Python, MATLAB, C++: Yes Under ...

  9. List of artificial intelligence projects - Wikipedia

    en.wikipedia.org/wiki/List_of_artificial...

    FreeHAL, a self-learning conversation simulator which uses semantic nets to organize its knowledge to imitate a very close human behavior within conversations. [55] Gemini, a family of multimodal large language model developed by Google's DeepMind. [56] Drives the Gemini chatbot, formerly known as Bard. [57]