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:
This is a great video that explains a lot of what I’ve observed from students trying to machine learning, but put more eloquently than I could have said myself. =)
I’m always having to contend against students who have taken a super easy-peasy course, actually learned nothing, but believe they know everything. Then, when they come up against the real content, they believe it’s because the instructor is trying to make the course really “elite” or trying to make them feel “dumb” by including lots of math and/or programming that they can’t understand.
I (or any other instructor) did not invent these subjects
If the subject requires math, that’s because it does
If the subject requires programming, that’s because it does
We didn’t put math in there just to torture you. If you’re taking a math course, it’s probably going to have math in it.
A student gets frustrated because they don’t understand the real subject, but really they should be frustrated with the instructor who gave them the empty course that provided them with no skill and too much confidence.
This video is about software developers, but if you view it from the perspective of machine learning, everything still applies. Watch the video!