Jupyter Archive • 3rd man

27. October 2020 .NET Core, Apache Spark, Big Data, C#, Data Science, Docker, Jupyter, Learning

.NET for Apache Spark 1.0 is available

Microsoft has released version 1.0 of .NET for Apache® Spark™. The official announcement provides a brief overview of the basic features, performance and the future of .NET for Apache Spark.

It even mentions my docker images, which are now available for version 1.0.0, too. Some of my articles are mentioned on the Open Source Blog as well. A big Thank You to everyone supporting me by trying out the images, providing feedback or contributing in any other way.

Speaking of docker images: In case you didn’t know yet, there are now three different types of … more

21. October 2020 .NET Core, Apache Spark, Big Data, C#, Data Science, Docker, Jupyter, Learning

.NET for Apache Spark – interactive notebook Docker image

If you are reading this, you are probably aware of my .NET for Apache Spark Docker images that I’ve made available so far. Just recently I’ve added a development image that allows you to easily build .NET for Apache Spark with VS Code in a browser. Today I want to introduce you to the latest member of the family:

The .NET for Apache Spark interactive notebook Docker image.

Jupyter Notebooks

In case you are not aware of what Jupyter Notebooks are, here’s a quick summary quote from the Jupyter project site.

The Jupyter Notebook

… more

17. August 2020 .NET Core, Big Data, C#, Data Science, Jupyter, Python

NumSharp Cheat Sheet

Recently, I published a small example project to utilize the htm.core AI algorithm by consuming its REST API via C#. As the API also transfers serialized multi-dimensional NumPy arrays, I was looking for an easy way to get them back into C# objects. I’ve tried out a couple of approaches and finally decided on using the NumSharp library, as I wanted a solution that works on multiple platforms.

I find DataCamps’ Data Science Cheat Sheets very useful and was hoping to find something similar for NumSharp. Well, I didn’t, but obviously that gave me a … more

12. February 2020 Artificial Intelligence, Big Data, Docker, Jupyter, Machine Learning, Python

Using the htm.core-jupyter docker image

To make it easier to get started with some of my htm.core experiments or with htm.core in general, I thought it would make sense to provide a docker image with htm.core preinstalled. So here it is:
Please welcome the htm.core-jupyter image.

This image is using the scipy-notebook as foundation, with the htm.core package installed on top of it.

For a list of the other preinstalled python packages, just look here.
There are also a lot of other Jupyter docker images available. I recommend starting with the Jupyter Docker Stacks quick start page for a … more

4. February 2020 Artificial Intelligence, Big Data, Development, Hierarchical Temporal Memory, Learning, Python

Hierarchical Temporal Memory – part 1 – getting started

Prologue

I came across the concept of Hierarchical Temporal Memory (HTM) and its implementation a while ago, and am still very fascinated about this approach to artificial intelligence.

When, about one year ago, the active development shifted towards the community fork named htm.core, which supports Python 3, it became finally time to have a closer look and try it out by myself.

BTW: According to this forum post, there are no plans to upgrade the older NuPIC library to Python 3.

A lot of documentation about the theory of HTM is available at numenta.org… more

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