Siraj Raval has a video exploring a paper about genomics and creating reliable machine learning systems.

Deep learning classifiers make the ladies (and gentlemen) swoon, but they often classify novel data that’s not in the training set incorrectly with high confidence. This has serious real world consequences! In Medicine, this could mean misdiagnosing a patient. In autonomous vehicles, this could mean ignoring a stop sign. Machines are increasingly tasked with making life or death decisions like that, so it’s important that we figure out how to correct this problem! I found a new, relatively obscure yet extremely fascinating paper out of Google Research that tackles this problem head on. In this episode, I’ll explain the work of these researchers, we’ll write some code, do some math, do some visualizations, and by the end I’ll freestyle rap about AI and genomics. I had a lot of fun making this, so I hope you enjoy it!

Likelihood Ratios for Out-of-Distribution Detection paper: https://arxiv.org/pdf/1906.02845.pdf 

The researcher’s code: https://github.com/google-research/google-research/tree/master/genomics_ood

Siraj Raval gets back to inspiring people to get into AI and pokes fun at himself.

Almost exactly 4 years ago I decided to dedicate my life to helping educate the world on Artificial Intelligence. There were hardly any resources designed for absolute beginners and the field was dominated by PhDs. In 2020, thanks to the extraordinary contributions of everyone in this community, all that has changed. It’s easier than ever before to enter into this field, even without an IT background. We’ve seen brave entrepreneurs figure out how to deploy this technology to save lives (medical imaging, automated diagnosis) and accelerate Science (AlphaFold). We’ve seen algorithmic advances (deepfakes) and ethical controversies (automated surveillance) that shocked the world. The AI field is now a global, cross-cultural movement that’s not limited to academics alone. And that’s something all of us should be proud of, we’re all apart of this. I’ve packed a lot into this episode! I’ll give my annual lists of the best ML language and libraries to learn this year, how to learn ML in 2020, as well as 8 predictions about where this field is headed. I had a lot of fun making this, so I hope you enjoy it!

Will Kwan was told he wasn’t beautiful enough to be an Instagram model so he used a generative adversarial network to generate some beautiful Instagram people to pose for me.

Code:

Siraj Raval shows off examples of machine learning apps from his students.

If you’re wondering about my stance on the recent controversies around Siraj, I recorded a Data Point about that.

Machine Learning powers almost every internet service we use these days, but it’s rare to find a full pipeline example of machine learning being deployed in a web app. In this episode, I’d like to present 5 full-stack machine learning demos submitted as midterm projects from the students of my current course. The midterm assignment was to create a paid machine learning web app, and after receiving countless incredible submissions, I’ve decided to share my favorite 5 publicly. I was surprised by how many students in the course had never coded before and to see them building a full-stack web app in a few weeks was a very fulfilling experience. Use these examples as a template to help you ideate on potential business ideas to make a positive impact in the world using machine learning. And if you’d like, be sure to reach out and support each of the students I’ve demoed here today in any way can you offer. They’ve been working their butts off. Enjoy!

Presentation notebook: https://colab.research.google.com/drive/1m5aLHPnwIhVX8zgMvZUtK4iG9xSaMbk8

Speaking neural networks, here’s a live recording of my talk from Azure Data Fest Fall 2019 in Reston

In this session, I explain Neural Networks from the Ground Up

Neural networks are an essential element of many advanced artificial intelligence (AI) solutions. However, few people understand the core mathematical or structural underpinnings of this concept.

In this session, learn the basic structure of neural networks and how to build out a simple neural network from scratch with Python.

This episode was recorded live at the Azure Data Fest in Reston, VA on Oct 11, 2019.You can watch the entire live stream here: http://franksworld.com/2019/10/11/azure-data-fest-reston-live-stream/

Press the play button below to listen here or visit the show page at DataDriven.tv.

Continuing our live coverage of Azure Data Fest Fall 2019 in Reston is this session by Frank La Vigne.

Neural Networks from the Ground Up

Neural networks are an essential element of many advanced artificial intelligence (AI) solutions. However, few people understand the core mathematical or structural underpinnings of this concept.

In this session, learn the basic structure of neural networks and how to build out a simple neural network from scratch with Python.

This was recorded live at the Azure Data Fest in Reston, VA on Oct 11, 2019. You can watch the entire live stream here: http://franksworld.com/2019/10/11/azure-data-fest-reston-live-stream/

Press the play button below to listen here or visit the show page at DataDriven.tv.

Dani, a game developer, recently made a game and decided to train an AI to play it.

A couple of weeks ago I made a video “Making a Game in ONE Day (12 Hours)”, and today I’m trying to teach an A.I to play my game!

Basically I’m gonna use Neural Networks to make the A.I learn to play my game.

This is something I’ve always wanted to do, and I’m really happy I finally got around to do it. Some of the biggest inspirations for this is obviously carykh, Jabrils & Codebullet!

Siraj Raval just posted this video on defending AI against adversarial attacks

Machine Learning technology isn’t perfect, it’s vulnerable to many different types of attacks! In this episode, I’ll explain 2 common types of attacks and 2 common types of defenses using various code demos from across the Web. There’s some really dope mathematics involved with adversarial attacks, and it was a lot of fun reading about the ‘cat and mouse’ game between new attack techniques, followed by new defense techniques. I encourage anyone new to the field who finds this stuff interesting to learn more about it. I definitely plan to. Let’s look into some math, code, and examples. Enjoy!

Slideshow for this video:
https://colab.research.google.com/drive/19N9VWTukXTPUj9eukeie55XIu3HKR5TT

Demo project:
https://github.com/jaxball/advis.js