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  2. fast.ai - Wikipedia

    en.wikipedia.org/wiki/Fast.ai

    In the fall of 2018, fast.ai released v1.0 of their free open-source library for deep learning called fastai (without a period), sitting atop PyTorch. Google Cloud was the first to announce its support. [ 6 ]

  3. Jeremy Howard (entrepreneur) - Wikipedia

    en.wikipedia.org/wiki/Jeremy_Howard_(entrepreneur)

    Jeremy Howard (born 13 November 1973) is an Australian data scientist, entrepreneur, and educator. [1]He is the co-founder of fast.ai, where he teaches introductory courses, [2] develops software, and conducts research in the area of deep learning.

  4. Rachel Thomas (academic) - Wikipedia

    en.wikipedia.org/wiki/Rachel_Thomas_(academic)

    She served as an advisor for Deep Learning Indaba, a non-profit which looks to train African people in machine learning. In 2017 she was selected by Forbes magazine as one of 20+ "leading women" in artificial intelligence. [18] Thomas has also written on the application of data science and machine learning in medicine.

  5. Kaiming He - Wikipedia

    en.wikipedia.org/wiki/Kaiming_He

    Kaiming He (Chinese: 何恺明; pinyin: Hé Kǎimíng) is a Chinese computer scientist who primarily researches computer vision and deep learning. [2] He is an associate professor at Massachusetts Institute of Technology and is known as one of the creators of residual neural network (ResNet).

  6. LeNet - Wikipedia

    en.wikipedia.org/wiki/LeNet

    LeNet-5 was one of the earliest convolutional neural networks and was historically important during the development of deep learning. [1] In general, when "LeNet" is referred to without a number, it refers to the 1998 version, the most well-known version. It is also sometimes called "LeNet-5" or "LeNet5".

  7. Deep reinforcement learning - Wikipedia

    en.wikipedia.org/wiki/Deep_reinforcement_learning

    Deep learning methods, often using supervised learning with labeled datasets, have been shown to solve tasks that involve handling complex, high-dimensional raw input data (such as images) with less manual feature engineering than prior methods, enabling significant progress in several fields including computer vision and natural language ...

  8. Artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Artificial_intelligence

    Deep learning has profoundly improved the performance of programs in many important subfields of artificial intelligence, including computer vision, speech recognition, natural language processing, image classification, [113] and others. The reason that deep learning performs so well in so many applications is not known as of 2021. [114]

  9. Michael Fullan - Wikipedia

    en.wikipedia.org/wiki/Michael_Fullan

    Michael Fullan is the Global Leadership Director, New Pedagogies for Deep Learning. Deep Learning, as described by NPDL, is mobilized by four elements that combine to form the new pedagogies. They are: Learning Partnerships, Learning Environments, Pedagogical Practices, and Leveraging Digital.