KCDC: I failed.

Amazing Conference

First, I want to say how amazing KCDC was this week. From the hotel, the venue, the people, and the crowd were all top notch. I will measure the rest of the conferences against this one. The group that puts this on needs all of the praise.

Setting Up For Failure

Now, on to the point of this post. I bombed. I created a new presentation for this conference where I was going to focus on the deployment part of machine learning. I knew there are concerns with each extra external source you need to have work during a live presentation. I had 4. The first was a web site I could host in VS Code using Live Server. But, the next three all could cause major concerns. The Android application needed Android Studio and an emulator. Google Cloud required a billable project as well as a working AI platform. Azure requires a resource group, storage, and the machine learning studio. Way too many moving parts.

My Failure

I had a working presentation on Friday but on Saturday I tried to add a working Android application. This wasn’t smart. I am using my oldest’s computer. It isn’t a development machine. I shouldn’t have tried to make it one. Once I booted up Android Studio and started the emulator the laptop was toast. Presentation over.

The flow of my presentation SHOULD have been creating a Celsius to Fahrenheit TensorFlow model, then taking that and converting the model to TensorFlow.JS and put it in a web site. I would then convert the model to TensorFlow Lite and put it in an Android application. Then, I would take the full model and place it in Google clouds AI engine. Finally, I would place it in Azure.

Since Android Studio made the machine almost worthless I decided to do the Android part first and then close Android Studio. This made my presentation out of order with absolutely zero flow. But, at the moment, I didn’t know what else I could do.

After I got control of my computer back 30+ minutes into the 60 minute talk I was left with trying to cover as much as possible.

Changes

If I had to do this again (and I will at a future conference) I would skip the Android Studio and just show code. Running the demo isn’t worth it with my current hardware.

I also need to figure out how I want to handle the two clouds. I think that I will make those steps screenshots and just show the end result. I need to take some of the variance out of my talks.

Where to go now?

Well, I guess I learn from my mistakes and clean up the project. Add the screenshots to ensure a solid flow to the presentation. Then, only go to the working final solutions at the end of the project if I have time. I also need to dig into how I want to handle both of the clouds so I am not getting billed for all the days leading up to the presentation.

I am also planning to create a YouTube video of the presentation. That will let me do some editing as well as get into a flow.

Conclusion

I fell flat on my face at my largest conference yet. Now, I need to make some changes and move on. I need to ensure that my next presentation is great and not a waste of time for the people in the crowd that are giving me their time.

Links

My presentation slides: Github/ehennis
My presentation content: Github/ehennis

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Nebraska.Code Presentation

On July 14th, at 11am I am giving a talk about predicting basketball scores using Deep Learning. Here are those files.

Presentation: https://github.com/ehennis/Blog/blob/master/DeepLearningBasketball/DL-NebraskaCode.pptx

Code: https://github.com/ehennis/Blog/tree/master/DeepLearningBasketball/NebraskaCode

How I Create a Talk

It has been quite some time since I have added content to this blog. I figured with in person conferences getting started again that I would journal my process.

First, I need to find a topic that I either have some knowledge about or a desire to spend the time and effort to learn it to a level that is worth the audience’s time. My next two talks, Nebraska.Code and KCDC, fall into the first category. My next talks will need to be in the second category to expand my base ML knowledge. I am thinking about some of the language models but that might be a bit more than I can cover in an hour.

Second, I need to figure out what type of audience that I am going to speak with. If it is a specialized audience, like I did in The Netherlands with the School of AI, I can go a bit deeper into the actual technology. If it is a general developer conference I will keep it higher level as I don’t want to lose the beginners that want to get started while also allowing any advanced users to ask follow up questions.

Third, I need to decide how much I want to do in the slides versus a demo. Doing talks during COVID gave me some issues because it was much harder to keep the audience’s attention since they were at their desks versus in a conference room. Because of this, I went with more examples and made it seem like we were almost pair programming.

Fourth, I need to determine how long I need to make the talks. Most conferences give you an hour or hour and a half. My talk with Google on the Raspberry Pi was only 15 minutes. If my talk is going to be packages to be seen later I make them a little quicker since I don’t need to fill the time.

Finally, I need to figure out if this is a one off talk or something I will shop around to multiple conferences. The talks I am creating now are ones that I will want to shop around so I need to be aware of that when I am posting links, etc. Typically, I will use 90% of the same content but create all new files so that if there are any changes/optimizations I can update the talk without breaking my previous iterations of it.

MIT Deep Learning Continued

GitHub Repo: https://github.com/lexfridman/mit-deep-learning.
MIT DeepLearning Web Site: https://deeplearning.mit.edu/.

Deep Learning: State of the Art: https://www.youtube.com/watch?v=53YvP6gdD7U
This lecture was covering the breakthrough/interesting sections of machine learning. It isn’t about the benchmarks for certain datasets. I wish I had a good resource that would keep this list up to date so when I have some free time I can look through it.

Intro to Deep Reinforcement Learning: https://www.youtube.com/watch?v=zR11FLZ-O9M
This lecture what about RL, which I have already taken an entire semester (and can’t get enough of reading about it) so it was cool to see another teacher go at it. Obviously, 1 hr versus a semester is a lot different.

MIT Self-Driving Cars: State of the Art: https://www.youtube.com/watch?v=sRxaMDDMWQQ
This is similar to the previous state of the art lecture as it covers what Lex thinks is current and exciting. It is interesting looking at the L# levels. I wish they would publish this more often so common people can understand where we are at with autonomous cars.