4. Hugging Face Diffusers We are noticing the recent trend with applications using Diffusion Models either it can be Stable Diffusion or Dalle E Diffusers library provide you with pre trained diffusion models across vision and audio. Check this: https://
github.com/huggingface/di
ffusers
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@sumanth_077
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Hugging Face Diffusers: Pre-trained Diffusion Models for Vision and Audio
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Cog: Open-Source Tool for Packaging ML Models in Containers
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2. Cog Cog is an open-source tool that lets you package machine learning models in a standard, production-ready container. And you can deploy that packaged model to your own infrastructure. Check this: https://
github.com/replicate/cog -
Transformers from Hugging Face: Pretrained Models for Text, Vision, Audio
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1. Transformers from Hugging Face Transformers library provides you with thousands of pretrained models to perform tasks on text, vision, and audio. This will help you with leveraging the already existing models instead of building them from scratch. https://
github.com/huggingface/tr
ansformers
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Hidden Python Libraries for Machine Learning Beyond the Basics
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Pandas, Numpy, Matplotlib, Seaborn, and Tensorflow are the standard and most commonly used python libraries in ML. But here's a list of Libraries that you might be unaware of but are really handy in working with Machine Learning Problems:
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ML Pipeline Generation with Modular Customization Options
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That's a good question. Once you generate the ML pipeline It also provides you an option to focus on each specific part and modify it which makes it really handy.
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Automation enables focus on priority tasks and important matters
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Absolutely. It helps to focus on things that require attention by automating pretty general stuff
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Open-source ML Tools Content and Newsletter Subscription
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That's it. Thanks for reading. Follow @Sumanth_077 as I will be sharing content on more open-source ML Tools in the coming threads. If you like this content you will definitely love my newsletter as well. Subscribe here: http://
mlnewsletter.substack.com -
Model Pipeline: Pre-processing, Feature Engineering, and Training
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Finally here of how it creates the Model Pipeline which is a 3-step process. 1. Pre-processing and cleaning 2. Feature Engineering 3. Model Building and Training Check the Below Image
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Training Predictive Models with Lightwood Mixers
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Once you get Python Code for the Problem you can then train the model on the data and run predictions. There are many default available predictive models in Lightwood called "Mixers" but it also supports you to create your custom mixers. Check this: https://
lightwood.io/mixer.html -
Pandas Data Loading and JSON-AI Code Generation Workflow
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Let's see how this works: Load data in Pandas and define a "ProblemDefinition" via dictionary specifying the name of the target column. Now Create a JSON-AI syntax using "json_ai_from_problem" function From Json AI generate a python code using "code_from_json_ai"