adaptive
MindsDB
adaptive | MindsDB | |
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11 | 78 | |
1,113 | 21,312 | |
1.4% | 1.5% | |
6.2 | 10.0 | |
6 days ago | 7 days ago | |
Python | Python | |
BSD 3-clause "New" or "Revised" License | GNU General Public License v3.0 or later |
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adaptive
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I made a Python package to do adaptive learning of functions in parallel [P]
Imagine you have a drawing with lots of hills and valleys, and you want to understand the shape of the landscape. Instead of measuring the height at every single point, Adaptive helps you measure the height at the most important points. It focuses on areas where the hills and valleys change a lot, so you can understand the drawing with fewer measurements.
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I made a Python package to do adaptive sampling of functions in parallel [OC]
Yes! Check it out at https://github.com/python-adaptive/adaptive/
Explore and star βοΈ the repo on github.com/python-adaptive/adaptive, and check out the documentation at adaptive.readthedocs.io.
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Introducing Markdown Code Runner: Automatically execute code blocks in your Markdown files! π
Also, Quatro will require a YAML annotation at the top of the file that will always be visible, e.g., a notebook on GitHub: https://github.com/python-adaptive/adaptive/blob/main/docs/source/tutorial/tutorial.DataSaver.md
- Does Julia have something like pythons adaptive?
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Graph plotting software for nasty function
Getting Python to calculate the equation for you shouldn't be a problem. The problem is that it may be having trouble figuring out which points to sample from. Using a uniformly spaced set of points won't necessarily result in the best looking curve, especially after interpolation. There is the adaptive package which does smart sampling of expensive functions. The idea is you give the function, and the adaptive library will learn the best x values to use and also return f(x).
MindsDB
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Whatβs the Difference Between Fine-tuning, Retraining, and RAG?
Check us out on GitHub.
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How to Forecast Air Temperatures with AI + IoT Sensor Data
If your data lacks uniform time intervals between consecutive entries, QuestDB offers a solution by allowing you to sample your data. After that, MindsDB facilitates creating, training, and deploying your time-series models.
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Fine-tuning a Mistral Language Model with Anyscale
MindsDB is an open-source AI platform for developers that connects AI/ML models with real-time data. It provides tools and automation to easily build and maintain personalized AI solutions.
- Vanna.ai: Chat with your SQL database
- FLaNK Weekly 08 Jan 2024
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MindsDB Docker Extension: Build ML powered apps at a much faster pace
MindsDB combines both AI and SQL functions in one; users can create, train, optimize, and deploy ML models without the need for external tools. Data analysts can create and visualize forecasts without having to navigate the complexities of ML pipelines.MindsDB is open-source and works with well-known databases like MySQL, Postgres, Redit, Snowflakes, etc.
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How Modern SQL Databases Are Changing Web Development - #4 Into the AI Era
Mindsdb is a good example. It abstracts everything related to an AI workflow as "virtual tables". For example, you can import OpenAI API as a "virtual table":
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ππ 23 issues to grow yourself as an exceptional open-source Python expert π§βπ» π₯
Repo : https://github.com/mindsdb/mindsdb
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AI-Powered Selection of Asset Management Companies using MindsDB and LlamaIndex
MindsDB is an AI Automation platform for building AI/ML powered features and applications. It works by connecting any data source with any AI/ML model or framework and automating how real-time data flows between them. MindsDB is integrated with LlamaIndex, which makes use of its data framework for connecting custom data sources to large language models. LlamaIndex data ingestion allows you to connect to data sources like PDFβs, webpages, etc., provides data indexing and a query interface that takes input prompts from your data and provides knowledge-augmented responses, thus making it easy to Q&A over documents and webpages.
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Using Large Language Models inside your database with MindsDB
Now, imagine if you can deploy these highly trained models in your database to get insights, make predictions, understand your users, auto-generate content, and more. MindsDB makes this possible! MindsDB is an open-source AI database middleware that allows you to supercharge your databases by integrating various machine learning (ML) engines.
What are some alternatives?
tensorflow - An Open Source Machine Learning Framework for Everyone
scikit-learn - scikit-learn: machine learning in Python
H2O - H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
Keras - Deep Learning for humans
postgresml - The GPU-powered AI application database. Get your app to market faster using the simplicity of SQL and the latest NLP, ML + LLM models.
gym - A toolkit for developing and comparing reinforcement learning algorithms.
CapRover - Scalable PaaS (automated Docker+nginx) - aka Heroku on Steroids
xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
lightwood - Lightwood is Legos for Machine Learning.