Skip to content

Maximum Likelihood Estimation - how neural networks learn | Chan`s Jupyter #98

@utterances-bot

Description

@utterances-bot

Maximum Likelihood Estimation - how neural networks learn | Chan`s Jupyter

In this post, we will review a Maximum Likelihood Estimation (MLE for short), an important learning principle used in neural network training. This is the copy of lecture “Probabilistic Deep Learning with Tensorflow 2” from Imperial College London.

https://goodboychan.github.io/python/coursera/tensorflow_probability/icl/2021/08/19/01-Maximum-likelihood-estimation.html

Metadata

Metadata

Assignees

No one assigned

    Labels

    No labels
    No labels

    Projects

    No projects

    Milestone

    No milestone

    Relationships

    None yet

    Development

    No branches or pull requests

    Issue actions