I have to disagree with The Deep Learning book. I don't find it a good book for anyone. For beginners it's too advanced/theoretical and for experienced ML scientists it's entirely too basic. I very much agree with this review on Amazon [1].
For the former, I would recommend Hands-On Learning with Scikit-Learn and Tensorflow
+1 for Elements. I started with Introduction to Statistical Learning and then graduated to Elements as I learned more and grew more confident. Those are fantastic books.
As an engineer who hadn't studied that type of math in quite a while, Elements was pretty tough and I was getting stuck a lot.
ISLR introduces you to many of the same topics in a less rigorous way. Once I was familiar with the topics and had worked through the exercises, Elements became much easier to learn from.
If you reading Elements is difficult then I would recommend Introduction.
I'm not sure if reading Introduction will prepare you for Elements so much as it will just give you some knowledge you can use and see if it makes sense for you and what you want to do to go and (re)learn some of the math tidbits that you need for Elements.
Frank Harrell writes a lot of great stuff and his answers on the Cross Validated Stack Exchange site are worth just reading even if you didn't think you wanted to ask the question they reply to.
People seem to love this textbook - and understandably so because it's very approachable. But I really struggled with how informal the tone was, and how friendly it was. Perhaps I'd grown too accustomed to the typical theorem -> proof -> example -> problem set format.
>>`"so your first mistake was using my code, but since you are clearly reading this
let it be known that mutuals.py lets you create a mutuals list on the hell
site known as "twitter dot com".`
I think your thinking of just a classic collaborative filtering recommendation system.A simple w2v system would take into account all words, then have to be filtered by words that are equal to subreddits. Although, I may have misunderstood your suggestion.
I don't think that was the case for the fellowship.
Applied after the 2 days with my team!
Didn't get a view on anything not even a click. Again, yeah it might be the case that it was nothing interesting to them and just skipped it. But to be honest it's a bit disappointing to see no indication that someone have actually looked at 2 days of work.
Because in a startup life, 2 days (48 hours) would be enough to build and ship a feature to a customer.
I agree though with the regular YC applications, I've heard that advice more than a few times from past alums.
- subbreddit: https://www.reddit.com/r/shutdown/
- podcast: failory.com