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  2. International Phonetic Alphabet chart - Wikipedia

    en.wikipedia.org/wiki/International_Phonetic...

    The following is the chart of the International Phonetic Alphabet, a standardized system of phonetic symbols devised and maintained by the International Phonetic Association.

  3. Epigenetics in learning and memory - Wikipedia

    en.wikipedia.org/wiki/Epigenetics_in_learning...

    Miller and Sweatt demonstrated that rats trained in a contextual fear conditioning paradigm had elevated levels of mRNA for DNMT3a and DNMT3b in the hippocampus. [4] Fear conditioning is an associative memory task where a context, like a room, is paired with an aversive stimulus, like a foot shock; animals who have learned the association show higher levels of freezing behavior when exposed to ...

  4. Reinforcement learning - Wikipedia

    en.wikipedia.org/wiki/Reinforcement_learning

    Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent ought to take actions in a dynamic environment in order to maximize the cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and ...

  5. Z shell - Wikipedia

    en.wikipedia.org/wiki/Z_shell

    The Z shell (Zsh) is a Unix shell that can be used as an interactive login shell and as a command interpreter for shell scripting. Zsh is an extended Bourne shell with many improvements, including some features of Bash, ksh, and tcsh. Zsh was created by Paul Falstad in 1990 while he was a student at Princeton University.

  6. Feature scaling - Wikipedia

    en.wikipedia.org/wiki/Feature_scaling

    In machine learning, we can handle various types of data, e.g. audio signals and pixel values for image data, and this data can include multiple dimensions. Feature standardization makes the values of each feature in the data have zero-mean (when subtracting the mean in the numerator) and unit-variance.

  7. Zero-shot learning - Wikipedia

    en.wikipedia.org/wiki/Zero-shot_learning

    Zero-shot learning (ZSL) is a problem setup in deep learning where, at test time, a learner observes samples from classes which were not observed during training, and needs to predict the class that they belong to.

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