And artificial Intelligence Intelligence
This is the first of what I hope will be many more Versioning updates focused on particularly important subjects - this time, machine learning and AI. Hopefully this will start many of you on the road to creating a tool that will take over the world! (Disclaimer: if you accidentally invent Skynet as a result of reading these links, I’m not to blame.)
A guide to how deep learning works, aimed at “everyone” [medium.freecodecamp].
Three ways to think about technology (and therefore AI) [cyberselves].
And a glossary of AI terms [thenextweb]. (Yes, I kinda jammed machine learning and AI together with this update.)
Starting points and tutorials
5 ways to get started with machine learning [sitepoint].
Exercises, lessons, lectures from experts, case-studies: Google’s crash course in machine learning is pretty hefty[developers.google].
A video guide to writing your first machine learning code [youtube/googledevelopers].
TensorFlow [tensorflow] is the open source machine learning network that might be your best bet early on, so here’s a TensorFlow tutorial full of examples to get you started [github/aymericdamien].
Helpful cheat sheets for Python-based machine learning [startupsventurecapital].
A simplified MultiAgent Python Snake game via deep reinforcement learning [youtube/crazymuse].
How to start trading cryptocurrency with machine learning [wildml]. Just in case you wanted to jam two very trendy ideas together!
🌟 “The ultimate guide” to deep learning for devs [zerotodeeplearning].
🌟 10 machine learning algorithms you should know in 2018 [bigdatanews.datasciencecentral].
🌟 A guide to the basics of natural language processing in Node [webdesignerdepot].
🌟 An intro to TensorFlow Probability [medium/tensorflow], a toolbox for when you need to do things like quantify uncertainty, you want a generative model of data, stuff like that.
🌟 The top 10 algorithms for machine learning, explained [towardsdatascience].
I’m a Model, You Know What I Mean?
Tools and resources
The complete collection of Facebook’s downloads and projects [research.fb].
And another collection of TensorFlow models [github/sarasra].
Google’s Cloud TPUs offer faster machine learning [cloud.google].
When your research starts to hit its stride, Lucid [github/tensorflow] is a collection of tools and infrastructure for research in neural network interpretability.
🌟 MUNIT [github/nvlabs] is a Python library offering unsupervised image-to-image translation. There are examples in there and they are striking!
🌟 TwinGAN [github/jerryli27] is a machine learning library that’ll let you do things like convert a photo to an anime drawing.
AI Can’t Believe It
Examples of AI doing amazing things [medium/archieai].
Finally, because of course: MariFlow [youtube/sethbling] is a self-driving Mario Kart example using a recurrent neural network.
There’s your intelligence about artificial intelligence - this will be updated over time. And more of these, on different subjects, will come through to paid subscribers soon! Let me know what you’d like to see, and sign up so you don’t miss out!
Curated by Adam