NumerAI Tournament

I was sent a link about NumerAI’s tournament. The idea is that the knowledge of the crowd is better than the knowledge of a few. A bunch (2,424 as of right now) of different models are created based on the same dataset and then the company uses all of them to predict price movement. My assumption is that something like a random forest is used.

I am going to use this opportunity to create a web series about my experiences. This should keep me busy and my data science skills sharp until next basketball season when I release my soon to be build models

GDG San Diego Talk

Overview

This will be the fourth time I am giving this talk about my work on the Raspberry Pi. I last gave it at the Google Developers ML Summit and it was recording and that was kind of odd. We will see if this one is a little better with some audience feedback.

TensorFlow Lite

Since I had some extra time I was able to implement TensorFlow Lite on the Raspberry Pi. I went to the TF Quickstart [Link] page and since I had already converted the Keras model into a tflite model I only needed the interpreter. This allowed me to not have to have the full TF install*.

*I still used the Keras preprocessing and decoding methods that came with the MobileNetV2 model. I probably should have not done that but at this point I don’t think it matters as much. Maybe in the future I will do ONLY TF Lite.

Links

Original Blog Series: Here
Meetup: https://gdg.community.dev/events/details/google-gdg-san-diego-presents-tensorflow-lite-on-the-raspberry-pi/
Code: Main, MobileNetV2Base, and PiCameraManager
Presentation: ImageDetection/GDG-SanDiego.pptx

Heartland Developers Conference Talk

Overview

This is a version of my talk about adding some image detection onto the Raspberry Pi. For the Paris talk, I only had 20 minutes and for GDG Denver I had 90 but didn’t use all of it so this length should be about right.

TensorFlow Lite

Since I had some extra time I was able to implement TensorFlow Lite on the Raspberry Pi. I went to the TF Quickstart [Link] page and since I had already converted the Keras model into a tflite model I only needed the interpreter. This allowed me to not have to have the full TF install*.

*I still used the Keras preprocessing and decoding methods that came with the MobileNetV2 model. I probably should have not done that but at this point I don’t think it matters as much. Maybe in the future I will do ONLY TF Lite.

Links

Original Blog Series: Here
Meetup: https://www.heartlanddc.com/
Code: Main, MobileNetV2Base, and PiCameraManager
Presentation: ImageDetection/HDC.pptx

Iowa State Google Developer Student Club Presentation

I have tried to get a student club created at Iowa State ever since I became a GDE. Well, the people at Google with some real pull were able to find some great students to lead it. Well, I am going to give a presentation on 9/9 about my work with deep learning and trying to predict basketball scores. Hopefully, they will get some entertainment and learn something.

DSC Link: https://dsc.community.dev/events/details/developer-student-clubs-iowa-state-university-presents-machine-learning-workshop-with-evan-hennis-google-developer-expert/

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