Projects-Archive
lightwood
| Projects-Archive | lightwood | |
|---|---|---|
| 1 | 2 | |
| 21 | 421 | |
| - | 1.4% | |
| 0.0 | 9.0 | |
| over 2 years ago | 15 days ago | |
| Jupyter Notebook | Python | |
| - | GNU General Public License v3.0 only |
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Projects-Archive
lightwood
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[D] What would a good ML take home test look like for you?
Create a very detailed issue about this (bonus points, you can use the same thing for all candidates to have a fair evaluation). Here's an example.
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Launch HN: MindsDB (YC W20) – Machine Learning Inside Your Database
3. A decoder that is trained to generate images takes that representation and generates an image1.
Note: above is a good illustrative example, in practice, we're good with outputting dates, numerical, categories, tags and time-series (i.e. predicting 20 steps ahead). We haven't put much work into image/text/audio/video outputs
You should be able to find more details about how we do this in the docs and most of the heavy lifting happens in the lightwood repo, the code for that is fairly readable I hope: https://github.com/mindsdb/lightwood
What are some alternatives?
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pyprobml - Python code for "Probabilistic Machine learning" book by Kevin Murphy
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probability - Probabilistic reasoning and statistical analysis in TensorFlow
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funsor - Functional tensors for probabilistic programming