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Posts Tagged ‘machine learning’
Using repl.it with Machine Learning for Kids
Sunday, May 10th, 2020Using TensorFlow with IBM Event Streams
(Kafka + Machine Learning = Awesome)
Thursday, October 31st, 2019
In this post, I want to explain how to get started creating machine learning applications using the data you have on Kafka topics.
I’ve written a sample app, with examples of how you can use Kafka topics as:
- a source of training data for creating machine learning models
- a source of test data for evaluating machine learning models
- an ongoing stream of events to make predictions about using machine learning models
I’ll use this post to explain how it works, and how you can use it as the basis of writing your first ML pipeline using the data on your own Kafka topics.
Explaining machine learning with decision trees
Sunday, August 18th, 2019Machine Learning for Kids now includes interactive visualisations that explain how some of the machine learning models that children create work.
The tool lets children learn about artificial intelligence by training machine learning models, and using that to make projects using tools like Scratch. I’ve described how I’ve seen children learn a lot about machine learning principles by being able to play and experiment with it. But I still want the site to do more to explain how the tech actually works, and this new feature is an attempt to do that.
Curated sample training datasets for Machine Learning for Kids
Wednesday, June 26th, 2019Machine Learning for Kids now includes support for a curated collection of training data sets, to enable children to create different types of machine learning projects.
The tool lets children make things using machine learning. The principle I’ve worked to is that children train their own machine learning models, as doing this is a great way to teach them about how this tech works.
Preparing their own training data is a useful exercise, but it is time-consuming. Project worksheets I’ve written so far have all been written with the assumption that the student will prepare the training data within a single lesson. This has been a limiting factor on the kinds of ML projects I’ve been able to include.
How to increase your social impact
Sunday, June 16th, 2019This is a talk I gave at an event about how we are able to make a social impact through volunteering and community projects.
From teaching his kids how to use #ML to play Rock, Paper, ?? to @MLforKids @dalelane created his passion for #machinelearning learning into a movement enabling schools around the ? @sap4good #experienceday #sociallearning pic.twitter.com/nDLpdlfqVn
— Eva Klingbeil (@EvaKlingbeil) April 29, 2019
I’ve written before about how I made Machine Learning for Kids. But this talk had a different focus.
For this presentation, I looked to see if there are any general lessons that could be learned from my experience, to let me offer a little advice for people working in large companies like IBM about how to increase the impact of their volunteering efforts.
IMPULSe talk by @dalelane from IBM on "Making IBM technologies accessible to schools": How do your make your passion project happening?#sociallearning #experienceday #passionproject pic.twitter.com/KuybZkPSga
— Felix Harling (@FelixHarling) April 29, 2019
Noughts and Crosses AI demo for Science Fair events
Monday, May 28th, 2018A hands-on demo for use at a small STEM event – letting kids train a simple AI system by playing a few games of noughts and crosses.
I prepared an activity for a STEM event in London this week. The idea was to make something for a Science Fair sort of event – where children will be walking past a stand, and might stop for a minute or two to try out an activity.
The objective was to come up with a hands-on demo that would enable a volunteer to talk to the children about machine learning.
As I’ve written it, I thought I’d share it here in case anyone else might find it useful for another event.
It’s based on the noughts-and-crosses activity that I’ve used before. Kids play noughts-and-crosses against a simple artificial intelligence system. The computer uses a machine learning model to decide where to make it’s moves. And that machine learning model will be trained throughout the event using the moves from every game so far.