https://datapad.readthedocs.io/en/latest/quickstart.html#ove...
144 karma · joined August 6, 2009
https://everyhue.me
https://datapad.readthedocs.io/en/latest/quickstart.html#ove...
https://forums.swift.org/t/text-streaming-in-standard-librar...
While these things are not difficult to solve with some minor additions to the standard library and documentation that emphasizes some basic conventions, the fact that we're on Swift6 and things like this aren't just available speaks to how much further the community needs to evolve to get the language ergonomics right for non apple-y things.
https://komonews.com/news/local/man-stranded-on-coronavirus-...
Some people can't get more selfish. Also, what a colossal failure on the part of customs and immigration.
Using SwiftUI is the first time ui programming on iOS has clicked for me.
For anyone getting into this, I suggest you take a look at apple's very well done tutorial series before using other sources:
https://developer.apple.com/tutorials/swiftui/tutorials
I spent about 3 days to go through the entire series and the time investment was well worth it.
Your example points to models that provide low quality uncertainty estimates, but that's not true for all deep learning models.
I believe it's these low quality uncertainty estimates that lead people to look toward "explainability" as a solution, but for the majority of use cases, I think people just want better uncertainty estimates so that they can "know when they're model doesn't know".
There are techniques now to get higher quality, calibrated, uncertainty estimates that don't suffer from the problems you mentioned and I've outlined these solutions in my posted link above.
Additionally if you're interested, there is some nice recent research from google on the subject:
https://ai.googleblog.com/2020/01/can-you-trust-your-models-...
and from oxford:
When people ask for explainable models, what they really want (in my opinion) is calibrated and robust uncertainty estimates .
Good uncertainty estimates would let them know when to trust a model's prediction and when to ignore it.
For example, a model trained to predict dog breeds should know nothing about cat breeds, and there should be some way to quantify when it doesn't know!
I've been doing a review of techniques that are becoming more popular in this area:
Another nice illustration of how when arrival rate is greater than departure rate we get overflowing queues:
Has anyone tried using this for development?
For quick tasks and scripts, I've found subprocess.check_call, and subprocess.check_output with shell=True are great tools for spawning subprocesses and quickly grabbing output. They're pretty straightforward to use.
When you started with your designer, how did you convey the type of look and feel that you wanted to give your business? Did you point them to other similar looking sites or was he/she the one that pushed forward the final vision and design (which BTW looks great)?
Recently, my team has been developing most of our newer network services using gevent. It's probably the closest thing to erlang that python has when it comes to programming asynchronous servers.
One of the things I always missed was this manhole feature, but while rummaging around the other day, I found this:
http://www.gevent.org/gevent.backdoor.html
Haven't tried it yet, but it looks like it will achieve the same task :)
Like K&R, it's style is colloquially terse, yet each sentence is chock full of information. It's a great book for someone who's already coming from another language like Python.
If you're into graphics programming, then http://love2d.org/ would be the perfect vehicle for you to get into Lua while you read PIL.
This is my blog.
FWIW, I've actually switched away from using the script mentioned in the link posted by OP and have moved towards using an improved version below.
http://www.huyng.com/bashmarks-directory-bookmarks-for-the-s...
This new version has 3 commands:
- "s" for save current directory as a bookmark
- "g" for jump to bookmark's directory
- "l" for list all bookmarks.Specifically, CouchDB's ability to distribute databases to clients seems like the ideal feature for this technology.
"CouchDB is a peer based distributed database system. Any number of CouchDB hosts (servers and offline-clients) can have independent “replica copies” of the same database, where applications have full database interactivity (query, add, edit, delete). When back online or on a schedule, database changes are replicated bi-directionally."
The manual directory alias naming means that you can target the folder that you want much more accurately. Combined with tab-completion, this thing has become a huge productivity booster for me.
link: http://www.huyng.com/bashmarks-directory-bookmarks-for-the-s...
Send me an email (see the link in my profile) . I can help you get up to speed with Django, real-time systems, and the web industry if you can teach me something valuable about the defense industry.
That's the deal maker for me... To have custom defined events. Correct me if I'm wrong but even Twisted doesn't have this facility?
I do a lot of jQuery as well but have been frustrated lately by how it's simplicity sometimes also makes it hard to maintain. For example as your app grows, you'll inevitably run into problems where your selectors no longer work because of refactoring and etc... Have you faced the same challenges? If not, what do you do to keep down the complexity?