What would you guys recommend for someone like me who has a strong background and education in ML algorithms (a PhD in a similar field) and math however but has very little formal programming skills. When it comes to coding I usually find myself hacking together code with the intention to come back to it later but never do. I have never had a problem finding and keeping jobs but have always felt a little insecure when it comes to writing code with CS majors, even ones with just a few years out of college. Any good material to help get me up to speed on the CS part in particular with common ML problems?
Basically, I'm confident that I can get any job done and working. However I am insecure or don't always know how to answering questions about how efficient / fast/ a block of code is. Also, code architecture and professional coding styles. And finally the sort of classical coding interview questions that most CS majors would learn in school but are not always relevant in daily use but seem to be asked often.
Those are all goals that with a little work should be surmountable.
When CS majors or programmers estimate how fast/efficient code is they generally are intuitively applying big 'O' notation or some similar concept. This can be gained from an intro level algorithms class. Or you could look up and read about the topic.
The classical coding interview questions mostly stem from an intro algorithms and data structures class. Or you could get a book on interview prep and work through a couple a day.
Finally, the coding style issue usually is ancillary knowledge and not found (or at least not done well) in typical CS curricular. I would suggest the book "Clean Code" by Robert Martin. It goes through code smells, commenting, general style, and is well written.
So overall, you could look at Algorithms and Data Structures MOOC's. Also read a book on CS interview prep and writing maintainable code. Hope this helps a little. Good Luck!
As someone who works, studies, and has published in this area or work, there is nothing new or novel about what they did. They only repeated work and ideas that have been around for a while and hyped it up.
I spent lots of time reading this and following the linked pages while in graduate school. I learned a lot but it didn't help graduation to come any quicker. https://en.wikipedia.org/wiki/List_of_paradoxes
Quick question for you all. Just two days ago I registered two domain names at dynu (not dyn). Early this morning I a cold call from a company in India who knew the domain names and my phone number and was calling to ask if I wanted them to help me manage my website cheaply. Also, this morning I got a spam text from someone who claimed to by godaddy offering the same thing. Now I protect my number really well so this is the first time in 5+ years that I ever got spam texts or calls to my number. Do you think Dynu was also hacked?! Or maybe Dynu sells client numbers (which is how the guy in India claimed to get my number) and it was just by random chance that this happened at the same time as the Dyn hack.
Agree with shortstuffsushi that this is just someone getting your domain name info and spamming you. It sadly happens all the time.
Go to http://whois.icann.org/en and enter your domain name and see what info is public about you. If all your info is public, you may want to see if your registrar offers "private" registration where your info does not appear in WHOIS.
Fwiw, this isn't a hack, this is a DDoS (denial of service). It seems almost certain that your information was either given out by dynu, or your WHOIS record isn't protected. Check your domains out with your favorite WHOIS tool first. Otherwise... time for an awkward conversation with dynu.
There are startups that in fact ONLY have government contracts. There are mechanisms that allow small startups to be awarded contracts without the need to compete with larger companies. The advantage in this approach is that founders don't need to go find venture funding, allowing them to retain more ownership of their company. This gives them the ability to have more to offer to new and talented employees. The disadvantage... well the government work slowly (which is counter to the silicon valley culture of rapid development, growth, and sell).
This is true, deep learning can make feature selection / engineering easier. That being said, a deep learning method can be over kill for a large number of problems that ML is used to solve. The amount of data needed for the training set and amount of computational power needed for the training set is often not available or a huge effort. I believe in keeping things simple if possible and spending a little more thought of feature construction. However, it isn't best for all problems.
It is understandable why Google, Facebook, and the others want changes to US laws. They feel that the bad publicity is hurting revenue. It will be interesting to see stance of companies that gain from the bad publicity like those whose business is based on securing and encrypting private information. Many of them have used the government's bad behavior as a marketing platform.
