So for some background, I'm a junior software engineer for a big, old household-name company. I have a recent Bachelor's in Computer Engineering (basically electrical engineering/computer science hybrid, focusing on computer hardware and OS/embedded level software development), and most of my current work is classic c++/java development with some python, perl and SQL here and there.
Recently my small corner of the company has taken an interest in machine learning and other modern data analytics techniques, and is planning some informal programs to develop/brainstorm ideas for potential applications of said techniques to our line of work. Given that I've always been interested in such things in the abstract, I'd like to jump aboard. However, aside from a passing familiarity with some of the high-level concepts (aka reading Wikipedia when I'm bored) I have zero direct experience with machine learning and data analytics in general. So I'm looking to learn as quickly as practical in my spare time.
I started to search through past HN comment threads for resources, then figured it might be quicker if I just asked. :)
Given the circumstances, if possible I'd like resources that involve applications. Theory is fun, and over the medium-term I will enthusiastically dive deep, but my immediate goal is to have the capacity to deliver something practical; that's dictated by the company's timeline.
Thanks for your time!
After that, I recommend Stanford's CS231n. It's focused on computer vision tasks, but you can learn a lot about neural nets in general from it. The videos were recorded around a year ago I think, so it's up to date with the latest deep learning techniques and libraries. For some reason the videos were taken down from the CS231n site but you can watch them here: https://www.youtube.com/watch?v=g-PvXUjD6qg&list=PLlJy-eBtNF...