My notes from yesterday were a little bit unorthodox. A lot of them were just links or a couple words to learn more about. I’m going to head over to the conference here in a little bit. At the end of my Day 2 notes I’m going to just add a little bit of commentary about the talks I attended.
Day 2: Recap of what I learned and what talks I attended…
Business Track: Virtualizing ML/AI and data science workloads (Michael Zimmerman) – This talk was very interesting and Michael extolled the virtues of reading the paper listed below.
General Keynote Session: Implicit deep learning and robustness (Laurent El Ghaoui) – This presentation is really solid. When I convert my presentation into a full paper I hope the formulas are as elegantly presented as what Laurent was able to produce. This talk really set the bar for walking and presenting formulas.
General Keynote Session: Pitfalls and panacea: AI and Cybersecurity (Wayne Chung) – It turns out my USB Type-C to HDMI cable works. It got an actual field test during this presentation. This turned out to be a very interesting talk full of security content and timelines. It was highly engaging.
Technical Track: AI methods for Formal Reasoning (Christian Szegedy) – Talked about moving AI efforts into a direction of true understanding and reasoning. I took a screenshot of a bunch of papers that are going to be added to my reading list.
Technical Track: Challenges in machine learning from model building to deployment at scale (Anupama Joshi) – This talk really dug into the ML software development life cycle and how that is managed.
Workshop: Tensorflow.js : Machine Learning In and Out of the Browser (Brian Sletten) – Talked about using machine learning at the edge. This was a longer several hour session that went into more detail. I did not take a ton of notes. This session was more hands on with the product.
Day 1: Recap of what I listened to during the day…
Workshop: Building Real World AI Solutions (Alexander Liss & Michael Liu) – This was 4 hours of workshop related to using TensorFlow for machine learning on AWS.
Technical Track (Finance): Group Theory, Chaos and Financial Time Series (Revant Nayar) – This is one of those presentations that needed a much better projector setup. It was hard to read the notations on the screen. I’m going to see if I can get the deck later to dig into it a little bit more.
Technical Track (Finance): Machine Learning In Finance (Chakri Cherukuri) – The visualizations used during this presentation were really top notch. I have been impressed with the team from Bloomberg.
Technical Track: Image Augmentations for Semantic Segmentation and Object Detection (Vladimir Iglovikov) – This talk was sort of a pitch for using Kaggle and competing in machine learning competitions. I actually wish Vladimir had just leaned into it and really talked about what it takes to compete and how that process worked. The talk really dived into the results and not the mechanics of how those competitions occur. I’m going to spend some time learning about Kaggle on the flight home tomorrow.
Finance Track : Data in Finance/Banking (Ryan Lee) – This talk could have gone a little bit deeper into the management and use of big data.
I have decided that getting a paper accepted at NIPS is a noble pursuit.