I’ve finally gotten around to adding a section on PyTorch basics to my course, Modern Deep Learning in Python (which already goes in-depth on Theano and Tensorflow).
As you recall, this course focuses on modern deep learning techniques such as adaptive learning rates and momentum, modern deep learning frameworks and GPU acceleration, and modern regularization techniques like dropout and batch normalization.
Too hot outside? Watch AI & Deep Learning videos instead!
I’ve been busy making free content and updates for my existing courses, so guess what that means? Everything on sale!
For the next 3 days, ALL courses on Udemy (not just mine) are available for just $9.99!
This is the lowest price possible on Udemy, so make sure you grab these courses while you have the chance.
For my courses, please use the coupons below (included in the links), or if you want, enter the coupon code: JULY2018.
For prerequisite courses (math, stats, Python programming) and all other courses, follow the links at the bottom.
Since ALL courses on Udemy on sale, for any course not listed here, just click the general (site-wide) link, and search for courses from that page.
BY THE WAY: Did you see my latest announcement about the massive updates I just made to my original Deep Learning with NLP course? It includes a brand new section called “Beginner’s Corner” which is designed to be useful for beginners to ML who aren’t quite ready for the rest of the course yet. If not: read more here.
ALSO: Got any requests? What do you want to learn about? (Doesn’t have to be Deep Learning or AI-related) Let me know!
First of all, this is a really cool finding for any of you who are into longevity research.
Secondly, this is a really cool example of a project that incorporates some machine learning, but also some hand-derived rules using domain expertise.
Basically, the researchers took a set of stem cells, then studied the properties of those cells. At each stage, they grouped the cells based on whether or not those cells would be capable of regenerating the flatworm. Finally, they ended up with the relevant cell.
(You might think of that as a decision tree)
At one stage, they use t-SNE to visualize clusters of different types of cells: