29 lines
2.0 KiB
Markdown
29 lines
2.0 KiB
Markdown
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# ML Management
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This will be important to keep a log of all of the tests that you'll want to run as you begin the EDA process.
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**Sacred**
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- [Sacred](https://github.com/IDSIA/sacred) is a fantastic open-source tool to use to pipeline the test process. As explained [here](https://towardsdatascience.com/managing-machine-learning-projects-226a37fc4bfa), it can really help to log all of the runs that you do with your model.
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- Use [CatalyzeX](https://chrome.google.com/webstore/detail/aiml-papers-with-code-eve/aikkeehnlfpamidigaffhfmgbkdeheil?hl=en) for code with ML papers.
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- Python [wrapper](https://github.com/nottheswimmer/dalle) for Dall-E API
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- PyTorch package to train and audit ML models for[ Individual Fairness](https://github.com/IBM/inFairness)
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- [Truss](https://www.baseten.co/) serves any model without boilerplate code
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- [WEKA](obsidian://open?vault=Coding%20Tips&file=Computers%2FPython%2FProjects%2FMachine%20Learning%2FWEKA) is a good resource data mining processes and machine learning testing
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- Collection of [wolfram](https://resources.wolframcloud.com/NeuralNetRepository/?source=nav) neural nets
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---
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**Deep Note & more ML repos**
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- [Deep Note](https://deepnote.com/workspace/windtelligent-e87f4ef4-a5f5-4f9b-8def-624a9e35da51/project/Welcome-2ef6e214-0da3-4ac5-9287-5e0d8ca5839f/%2Fnotebook.ipynb) is being used along with[ hugging face](https://huggingface.co/) to document an indepth analaysis on ML python tools
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- [BLOOM](https://huggingface.co/bigscience/bloom) 176 billion parameter LLM model created by researchers & FOSS
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- here are some [examples](https://github.com/Sentdex/BLOOM_Examples)
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---
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#### Further reading and tutorials:
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[Animated](https://nnfs.io/neural_network_animations) tutorials of Neural Networks
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Using [fast.ai](https://www.fast.ai/posts/2020-02-13-fastai-A-Layered-API-for-Deep-Learning.html)
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- As a reference, text generation has been happening since 2005 with [SCIGen](https://pdos.csail.mit.edu/archive/scigen/#talks) for instance.
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