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#AI

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Artificial Intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions) and self-correction.

Learn more.

Kidney Disease can be discovered from some parameters well known to the medical community. In this way, in order to help the medical community and computerized systems, especially AI, the scientist Akshay Singh published a very useful dataset for training ML algorithms in the detection/prediction of kidney disease. This publication can be found on the largest and best known data repository for ML, Kaggle at https://www.kaggle.com/datasets/akshayksingh/kidney-disease-dataset.

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In this article, I am trying to identify the multiple areas to develop the features we can able to do using python and machine learning.

Each hospital is every moment trying to improve its quality of service and efficiency using technology and services.

The healthcare sector is one of the very big and vast areas of service options available and python is one of the best technology for doing machine learning.

In every hospital, humans will come with some feelings, if this feeling will understand using technology is make a chance to provide better service.

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Hi developers!

We are starting InterSystems AI Programming Contest next week, and according to the rules, you can include some technology IRIS Features into your solutions, which will give you extra points in the voting.

Here are the technology bonuses for InterSystems AI Programming Contest!

1. IntegratedML usage - 2 expert vote points

IntegratedML is a new technology Introduced in InterSystems IRIS which you can use with InterSystems IRIS 2020.2 Advanced Analytics Preview release. IntegratedML:

  • Gives users the ability to create, train, and deploy powerful models from simple SQL syntax without requiring data scientists.
  • Wraps "best of breed" open source and proprietary "AutoML" frameworks including DataRobot.
  • Focuses on easy deployment to IRIS, so you can easily add machine learning to your applications.

Learn more in IntegratedML Resource Guide.

You can use with IntegratedML template.

2. Python Gateway usage - 1 expert vote point

Python Gateway is an addon to InterSystems IRIS which gives you the way to use Python in InterSystems IRIS environment:

  • Execute arbitrary Python code.
  • Seamlessly transfer data from InterSystems IRIS into Python.
  • Build intelligent Interoperability business processes with Python Interoperability Adapter.
  • Save, examine, modify and restore Python context from InterSystems IRIS.

Learn more about Python Gateway.

You can use the Python Gateway template, which includes IntegratedML too.

3. Docker container - 1 expert vote point

The application gets a 'Docker container' bonus if it uses InterSystems IRIS running in a docker container.

Both templates, IntegratedML template and Python Gateway template use docker so you can collect this bonus if you build your solution using these templates.

Or you can use any other Docker-based templates, published on Open Exchange.

Feel free to ask any questions about using the listed technologies.

Good luck in the competition!

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Hi everyone.
We are a team of company "Constructor" and we develop cutting edge cartographic systems. Recently the amount of image data skyrocketed so we want to give our users the ability to tie images to places automatically. For that, we want to use AI/ML technologies and we have a cool task for you.

https://cloud.mail.ru/public/pHbC/4r7Z58m6f/

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Episode 17 of Data Points features a roundtable conversation with Carmen Logue, Benjamin De Boe, and Thomas Dyar about the Analytics & AI area of the InterSystems technology stack. Learn from these product experts about the various technologies and partnerships that exist within the Analytics & AI space at InterSystems, how some customers use these tools, and what might be coming in the future.

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Keywords: Jupyter Notebook, Tensorflow GPU, Keras, Deep Learning, MLP, and HealthShare

1. Purpose and Objectives

In previous"Part I" we have set up a deep learning demo environment. In this "Part II" we will test what we could do with it.

Many people at my age had started with the classic MLP (Multi-Layer Perceptron) model. It is intuitive hence conceptually easier to start with.

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Hey Community!

The latest webinar, recorded by InterSystems Sales Engineers @Sergey Lukyanchikov and @Eduard Lebedyuk, is already on InterSystems Developers YouTube! Please welcome:

"Machine Learning Toolkit (Python, ObjectScript, Interoperability, Analytics) for InterSystems IRIS"

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Keywords: PyODBC, unixODBC, IRIS, IntegratedML, Jupyter Notebook, Python 3

Purpose

A few months ago I touched on a brief note on "Python JDBC connection into IRIS", and since then I referred to it more frequently than my own scratchpad hidden deep in my PC. Hence, here comes up another 5-minute note on how to make "Python ODBC connection into IRIS".

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