Hi Community, we would like to share the news about the "Bell State" example (a compact quantum computing program) automated using our ML Toolkit with a Rigetti QVM running in the background: 

This series of articles would cover Python Gateway for InterSystems Data Platforms. Execute Python code and more from InterSystems IRIS. This project brings you the power of Python right into your InterSystems IRIS environment:

We're back with more AI, more ML and more presenters!

We are pleased to invite you to the upcoming webinar in English: AI Robotization (Python, R, Interoperability) for InterSystems IRIS on November 7 at 11:00 EST!

Hi Everyone!

We are pleased to invite you to the upcoming webinar "Master Data Management and Your Enterprise" on 3rd of September at 11:00 EDT!

Hi Community:

Interested in getting hands-on with our technology? Come to Global Summit, where you'll find five experiences to choose from in the Experience Lab:

Hey Developers!

We are pleased to invite you to the upcoming  webinar "InterSystems MLToolkit: AI Robotization" on 18th of September at 10:00 (GMT+3)!

Hi Community:

If you're interested in the future of InterSystems technology, you won't want to miss these nine Global Summit sessions on our hottest technologies:

Roadmap Sessions

Hallo Community!

Bitte melden Sie sich zum Webinar "Machine Learning Projekte mit R erfolgreich umsetzen" an: 2. August 2019 um 10:30 Uhr.

Veranstaltungsdetails:

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.

Hi Community!

Please welcome a new video on InterSystems Developers YouTube Channel:

InterSystems IRIS from Spark to Finish

The last time that I created a playground for experimenting with machine learning using Apache Spark and an InterSystems data platform,  see Machine Learning with Spark and Caché, I installed and configured everything directly on my laptop: Caché, Python, Apache Spark, Java, some Hadoop libraries, to name a few. It required some effort, but eventually it worked.

The titular question was quite relevant and often discussed some thirty years ago. The thought went: “Sure, there are industries where computers are the norm, but in my industry we got just fine so far, the benefits are questionable, problems innumerable and unsolved. Can we continue as before or should we embrace this new technology?”

Today, everyone asks the same question but about Machine Learning and Artificial Intelligence. The doubts are the same – lack of expertise, lack of known path, perceived irrelevancy to the industry.

Yet, as before, the correct, even the only possible answer is a resounding yes. Read on to find out why.

Keywords:  Anaconda, Jupyter Notebook, Tensorflow GPU, Deep Learning,  Python 3 and HealthShare    

1. Purpose and Objectives

This "Part I" is a quick record on how to set up a "simple" but popular deep learning demo environment step-by-step with a Python 3 binding to a HealthShare 2017.2.1 instance .  I used a Win10 laptop at hand, but the approach works the same on MacOS and Linux.

Hi all. We are going to find duplicates in a dataset using Apache Spark Machine Learning algorithms.

Note: I have done the following on Ubuntu 18.04, Python 3.6.5, Zeppelin 0.8.0, Spark 2.1.1

Introduction

In previous articles we have done the following: