Machine Learning App Examples

Machine Learning App Examples

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

Neural Networks from the Ground Up

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.

Neural Networks from the Ground Up

Neural Networks from the Ground Up

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.

Game Developer Teaches an AI to Play the Game He Made

Game Developer Teaches an AI to Play the Game He Made

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!

Defense Against Adversarial Attacks

Defense Against Adversarial Attacks

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

 

Generative Modeling Applications

Generative Modeling Applications

Siraj Raval explores generative modeling technology.

This innovation is changing the face of the Internet as you read this. It’s now possible to design automated systems that can write novels, act as talking heads in videos, and compose music.

In this episode, Siraj explains how generative modeling works by demoing 3 examples that you can try yourself in your web browser. 

The Neural Qubit

The Neural Qubit

Siraj Raval wrote a research paper titled “The Neural Qubit” where he describe a quantum machine learning architecture inspired by neurons in the human brain.

Code: https://bit.ly/2jYh8u9
Paper: https://bit.ly/2ltxf3b

From the video description:

I’m pretty excited about quantum computing, it gives me a deep sense of wonder & confusion that i really enjoy. I’m so glad to be so confused (again)! I have lots more quantum machine learning papers to read in the coming weeks. In this episode, I describe the nonlinear motivations behind my paper, how i thought through the research process, and how i eventually came to some interesting results + conclusions. With the help of math, code, & manim(!) animations I’ll give it my best shot explaining some of the complex topics at the very edge of Computer Science I tackled. I hope you find it useful, enjoy!

Weight Agnostic Neural Networks

Weight Agnostic Neural Networks

Here’s an interesting look at weight agnostic neural networks and what problems they solve. Very interesting read.

Not all neural network architectures are created equal, some perform much better than others for certain tasks. But how important are the weight parameters of a neural network compared to its architecture? In this work, we question to what extent neural network architectures alone, without learning any weight parameters, can encode solutions for a given task.