Collecting image data for training machine learning models can take precious time and lots of Google image searches.
I built a tool that generates labeled data for you. Choose your categories and download.
Hi friend, I hope you enjoy the presentation!
In the talk I mention a number of code demos demonstrating neural nets in the browser. Links to each can be found here:
Tensorflow.jsis the Machine Learning library we looked at during the presentation.
make_moonsis a port from scikit's
make_moonsfunction. It generates two parabolas of x,y points.
ml-classifieris a library written in React for generating image classification models in your browser with drag n drop.
tfjs-visis a library for visualizing your models right in your app.
The Tensorflow.js docs contain a wealth of information about the library.
Another useful resource is the official Tensorflow.js examples repo, with code demonstrating a number of popular machine learning techniques with live demos.
The Tensorflow playground predates Tensorflow.js. It lets you build a neural net in your browser and see the training in real time.
There are a number of great courses for learning Artificial Intelligence.
Fast.ai puts out a series of video lectures aimed at hackers. The courses are done in Python.
Andrew Ng's Coursera course is widely recognized as one of the best introductions to AI and machine learning.
Here's the list of references from slides in the presentation, along with a number of cool links demonstrating what neural nets can do.
A description of linear and nonlinear data by John Sullivan.
Finally, if you enjoyed the talk, or have questions, feel free to shoot me a message on Slack or DM me on Twitter! I'd love to hear what you think.