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  2. T5 (language model) - Wikipedia

    en.wikipedia.org/wiki/T5_(language_model)

    T5 (Text-to-Text Transfer Transformer) is a series of large language models developed by Google AI introduced in 2019. [1] [2] Like the original Transformer model, [3] T5 models are encoder-decoder Transformers, where the encoder processes the input text, and the decoder generates the output text.

  3. 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 ...

  4. Kaggle - Wikipedia

    en.wikipedia.org/wiki/Kaggle

    Kaggle is a data science competition platform and online community for data scientists and machine learning practitioners under Google LLC.Kaggle enables users to find and publish datasets, explore and build models in a web-based data science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges.

  5. List of artificial intelligence projects - Wikipedia

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

    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 ...

  6. Timeline of machine learning - Wikipedia

    en.wikipedia.org/wiki/Timeline_of_machine_learning

    Deep learning spurs huge advances in vision and text processing. 2020s Generative AI leads to revolutionary models, creating a proliferation of foundation models both proprietary and open source, notably enabling products such as ChatGPT (text-based) and Stable Diffusion (image based). Machine learning and AI enter the wider public consciousness.

  7. Inception (deep learning architecture) - Wikipedia

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

    Since Inception v1 is deep, it suffered from the vanishing gradient problem. The team solved it by using two "auxiliary classifiers", which are linear-softmax classifiers inserted at 1/3-deep and 2/3-deep within the network, and the loss function is a weighted sum of all three: L = 0.3 L a u x , 1 + 0.3 L a u x , 2 + L r e a l {\displaystyle L ...

  8. XGBoost - Wikipedia

    en.wikipedia.org/wiki/XGBoost

    XG Boost initially started as a research project by Tianqi Chen [12] as part of the Distributed (Deep) Machine Learning Community (DMLC) group. Initially, it began as a terminal application which could be configured using a libsvm configuration file.

  9. Google Brain - Wikipedia

    en.wikipedia.org/wiki/Google_Brain

    The Google Brain team contributed to the Google Translate project by employing a new deep learning system that combines artificial neural networks with vast databases of multilingual texts. [21] In September 2016, Google Neural Machine Translation (GNMT) was launched, an end-to-end learning framework, able to learn from a large number of ...