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  2. Andrew Ng - Wikipedia

    en.wikipedia.org/wiki/Andrew_Ng

    Ng taught machine learning and Widom taught databases. The course on AI taught by Thrun led to the genesis of Udacity. [48] The seeds of massive open online courses (MOOCs) go back a few years before the founding of Coursera in 2012. Two themes emphasized in the founding of modern MOOCs were scale and availability. [48]

  3. Machine learning - Wikipedia

    en.wikipedia.org/wiki/Machine_learning

    Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases).

  4. TensorFlow - Wikipedia

    en.wikipedia.org/wiki/TensorFlow

    In May 2016, Google announced its Tensor processing unit (TPU), an application-specific integrated circuit (ASIC, a hardware chip) built specifically for machine learning and tailored for TensorFlow. A TPU is a programmable AI accelerator designed to provide high throughput of low-precision arithmetic (e.g., 8-bit ), and oriented toward using ...

  5. Online machine learning - Wikipedia

    en.wikipedia.org/wiki/Online_machine_learning

    In computer science, online machine learning is a method of machine learning in which data becomes available in a sequential order and is used to update the best predictor for future data at each step, as opposed to batch learning techniques which generate the best predictor by learning on the entire training data set at once. Online learning ...

  6. Google Brain - Wikipedia

    en.wikipedia.org/wiki/Google_Brain

    TensorFlow is an open source software library powered by Google Brain that allows anyone to utilize machine learning by providing the tools to train one's own neural network. [2] The tool has been used to develop software using deep learning models that farmers use to reduce the amount of manual labor required to sort their yield, by training ...

  7. Transformer (deep learning architecture) - Wikipedia

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

    The plain transformer architecture had difficulty converging. In the original paper [1] the authors recommended using learning rate warmup. That is, the learning rate should linearly scale up from 0 to maximal value for the first part of the training (usually recommended to be 2% of the total number of training steps), before decaying again.

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