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your-docusaurus-site.example.com | ||
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www.altexsoft.com
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| | | | | A dive into the machine learning pipeline on the production stage: the description of architecture, tools, and general flow of the model deployment. | |
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www.ethanrosenthal.com
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| | | | | How would you build a machine learning algorithm to solve the following types of problems? Predict which medal athletes will win in the olympics. Predict how a shoe will fit a foot (too small, perfect, too big). Predict how many stars a critic will rate a movie. If you reach into your typical toolkit, you'll probably either reach for regression or multiclass classification. For regression, maybe you treat the number of stars (1-5) in the movie critic question as your target, and you train a model using m... | |
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ml-ops.org
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| | | | | Machine Learning Operations | |
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www.paepper.com
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| | | Today's paper: Rethinking 'Batch' in BatchNorm by Wu & Johnson BatchNorm is a critical building block in modern convolutional neural networks. Its unique property of operating on "batches" instead of individual samples introduces significantly different behaviors from most other operations in deep learning. As a result, it leads to many hidden caveats that can negatively impact model's performance in subtle ways. This is a citation from the paper's abstract and the emphasis is mine which caught my attention. Let's explore these subtle ways which can negatively impact your model's performance! The paper of Wu & Johnson can be found on arxiv. | ||
