Thanks for the talk. A great overview of the topic.
Nicely delivered especially considering the speaker stepped in at the last minute.
Slides were pretty clear but perhaps a bit text heavy in places. From the back of the room, the bottom portion of the slides were hard to see.
Thanks again for a great talk.
Thanks for the talk. Very clear slides.
A bit rushed in places. Pro/cons slides were really nice.
At one stage I thought would be fun to work out how to implement an autowiring class, but then you showed us how it could work, dispelling some of the 'magic' discomfort.
Lose the hat next time.
Thanks again for a great talk.
Nice talk. One thing that would that would improve it is, in my opinion......never using a laser pointer.
Laser pointers are quite hard to see - it's better imo to use a presentation tool that allows you to highlight lines of text or put boxes around the thing you want people to focus on. https://revealjs.com/ is quite nice, and also adapts itself to different screen width/height ratios.
Also, using a laser pointer makes the speaker turn away from the audience to be able to look at the screen, which means you break eye contact, which makes it harder to stay engaged with the talk, as well as harder to hear for the people who are sitting on the side you've turned away from.
Great talk, especially after stepping in at the last minute. I thought this would mostly be over my head, but the concepts were well explained and I can now identify a couple of areas in upcoming projects where machine learning could possibly be applied.
Thank you for doing this! So impressive doing a last minute talk, wonderful intro.
I wasn't immediately sure what you meant by "If your reading PowerPoint it's AI".
Love the use of vintage memes for cats and dogs.
The distinction between Classification and Regression was really clear! Also for the following distinctions. Good stage presence.
I want a whole talk on how studies on Neural Networks are being applied backwards to biology! So fascinating. Weighting sounds interesting.
A lot of code on the simple neural network slide but you stepped through it really clearly. Helpful comments esp re seen/unseen values.
I'd never considered data preprocessing but it makes sense and you gave great examples.
Great last minute talk. Super appreciate stepping in with a relevant topic. Few bits you lost Menon in the algorithm part that felt a bit dry but the general overviews of how it works was one of the clearest explanation iv heard
Fantastic talk. Definitely going to have a look into the libraries for python. I'm definitely more interested in machine learning than I was before.
Nice talk, really wish people would stop saying “type hint” though and say parameter type instead.
Also maybe change the code example from UserModel to a simple service as not everyone might be familiar with UserModel if they haven’t used the frameworks those are used in.
Good talk. Nice clear slides.
- maybe pick a simple example that does something obvious and required, possibly a UserModel is a bit abstract.
- maybe spend a little longer explaining benefits.
- When you were talking about reflection you spoke about type hinting with on interfaces and how DI could wire in the concrete implementation. This section would benefit from a slide.
- Maybe speak a little slower, it seemed rushed in places
Good luck for when you present it at a conference.