pyprobml
lightwood
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| pyprobml | lightwood | |
|---|---|---|
| 3 | 2 | |
| 6,257 | 421 | |
| 1.7% | 4.0% | |
| 6.2 | 9.0 | |
| 4 months ago | 11 days ago | |
| Jupyter Notebook | Python | |
| MIT License | GNU General Public License v3.0 only |
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
pyprobml
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Best Possible Book Recommended for Machine Learning [Discussion] [D] [Recommendation]
Another great book is Kevin Murphy’s Machine Learning: A probabilistic approach. He just launched the second version of his book and he has a Python repo for the models and graphs: https://github.com/probml/pyprobml
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Probabilistic Machine Learning, Kevin Murphy (2nd edition, 2021)
This exists actually, it's not complete yet (I think?) but it covers a lot of the material in the book:
https://github.com/probml/pyprobml
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?
numpyro - Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.
MindsDB - The platform for customizing AI from enterprise data
prml - Repository of notes, code and notebooks in Python for the book Pattern Recognition and Machine Learning by Christopher Bishop
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
jaxopt - Hardware accelerated, batchable and differentiable optimizers in JAX.
nitroml - NitroML is a modular, portable, and scalable model-quality benchmarking framework for Machine Learning and Automated Machine Learning (AutoML) pipelines.
machine-learning-experiments - 🤖 Interactive Machine Learning experiments: 🏋️models training + 🎨models demo
probability - Probabilistic reasoning and statistical analysis in TensorFlow
lucid - A collection of infrastructure and tools for research in neural network interpretability.
Projects-Archive - This hacktober fest, the only stop you’ll need to make for ML, Web Dev and App Dev - see you there!
PRML - PRML algorithms implemented in Python
funsor - Functional tensors for probabilistic programming