Artificial Intelligence is the single most important endeavor ever under taken by humanity. If you care to learn the technical side of this venture, I’ve put together a short-and-growing list of resources to look at for introductory learning and exploration purposes.
Please enjoy and suggest additions.
from the course description:
“introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence. Upon completion of 6.034, students should be able to develop intelligent systems by assembling solutions to concrete computational problems”
from the intro:
“an introduction to the practice of deep learning through the applied theme of building a self-driving car. It is open to beginners and is designed for those who are new to machine learning, but it can also benefit advanced researchers in the field looking for a practical overview of deep learning methods and their application.”
Introduction to Neural Networks on the high-level ML platform Keras.
trying to make AI less “exclusive”. Practical courses and tutorials. Cool branding.
from the about page:
“GitXiv is a space to share collaborative open computer science projects. Countless Github and arXiv links are floating around the web. Its hard to keep track of these gems. GitXiv attempts to solve this problem by offering a collaboratively curated feed of projects. Each project is conveniently presented as arXiv + Github + Links + Discussion.”
This is the definitive college-level course on Machine Learning. It has nearly 12,000 reviews. I’m working through it presently. Includes a great intro/refresher to linear algebra (that I needed).
Thanks to Stanford for providing the material.
The world’s easiest introduction to Machine Learning
There’s also a video course on Lynda.
from the post:
Introduction to practical application of Q-learning and neural networks using TensorFlow.
How to build an image classifier in TensorFlow for poets.