00:11
AI voice tuner in Scratch
00:41
AI Pac-Man in Scratch
00:51
Describe the Glass
09:33
IBM Event Processing - API Enrichment demo
00:46
Using MobileNet in Scratch (no audio)
01:27
mobilenet demo (no audio)
02:04
updated Flink MATCH_RECOGNIZE demo
01:05
Flink can recognize when you're cheating
09:07
How to summarize weather sensor readings using Event Processing and watsonx
08:34
How to summarize stock trading activity using Event Processing and watsonx
01:10
Using Event Processing with watsonx to summarize weather sensor events
01:24
Using Event Processing with watsonx to summarize stock trading events
00:33
Use Scratch to predict if you like a book cover (no audio)
00:52
making a CAPTCHA solver in Scratch
00:53
Hoops training
00:48
Hoops
01:45
shoot the bug regression
05:09
AsyncAPI v3 demos
10:30
Machine learning in Scratch - demonstrating regression
01:50
linear regression in Scratch (prototype)
02:58
Creating a Slack token for your Kafka Connect connector
33:40
How to create a Kafka Connect connector
01:15
Our house, after a less than fun week
24:45
Event Processing - why don't I have results yet?
07:24
pigeon cam 5
06:27
pigeon cam 4 - feeding time
05:00
pigeon cam 3
05:07
pigeon cam 2
09:53
pigeon cam
00:56
Using MQTT to communicate between Scratch projects
00:13
Running the project in one browser
00:19
Connecting to an MQTT broker
00:58
gatecrasher gameplay
00:20
Carpool Cheats - testing
00:31
Carpool Cheats - training
02:01
walk to work hyperlapse
03:01
walk to work hyperlapse (3 mins)
03:28
walking home (timelapse attempt 2)
00:29
walking to work timelapse
08:15
IBM Event Automation - demo / runthrough
01:35
Machine Learning for Kids with EduBlocks
01:23
Bionic Bar
00:30
A Balanced Brew gameplay
48:57
How to write your own Scratch extension
00:20
my Nintendo Year in Review
02:29
playing with the Spotify API in Scratch - happier or sadder
00:29
playing with the Spotify API in Scratch - guess the tempo
00:43
playing with the Spotify API in Scratch - dancing in time to the music
00:17
playing with the Spotify API
00:14
Machine learning hasn't replaced the need to code - Machine Learning for Kids stories
01:43
Invisible artificial intelligence systems are everywhere
03:23
What happens when machine learning models are used for things they weren't trained to do
05:16
Bias and the responsibility of the machine learning developer
02:07
Inputs that aren't represented in training may not be recognised correctly
03:22
There are ways to deal with the 'black box challenge'
03:33
Machine learning doesn't always learn what we intend it to
03:13
How to use confidence scores
02:31
Collecting poorly handled inputs to add to training is a common practice
01:34
Machine learning can learn to recognize different types of patterns
02:16
Training and test data should be kept separate