id_ris
* knowing the maximum dimensions you can fit into your vehicle. Are you sure it'll fit in through the door or hatch and you'll be able to close it?
* knowing what cuts you can make at home, and which ones you need to have done at the lumber yard / big box retailer.
* if you need cuts done at the store, does that store offer that service? For example the Menards I shop at has an amazing selection of wood, but they won't cut any of it for me. The Lowe's has a smaller selection, but they do cuts for free.
* if you're having cuts at the store, make sure the saw is working before doing the hard work of choosing your lumber. Too many times to count I've spent 30 minutes getting all the pieces loaded on the cart and then noticed the "Out of Order" sign and had to leave empty handed.
* are you strong enough to lift the piece? Plywood comes in 4 foot by 8 foot sheets, and they can be heavy and awkward to move. A store employee can help you, but at some point you're on your own and will need to do it yourself, have some help, or fashion some tools to assist you.
* For cuts at the store, do you know what dimensions to tell the employee to cut at? Store cuts should be considered rough cuts than you true up later on. Make sure you account for this when dictating the cuts.
* Lastly, just assume you'll make mistakes early on. Luckily construction lumber in the US is pretty cheap, so you can afford a few lessons.
Yet swift is open source, and Apple and the community can fork it if they so choose. This is great news for me personally as an iOS developer and an ML noob who doesn't want to write Python. I can't comment on Julia because I have no experience with it, but I applaud the efforts to build the Swift ecosystem to challenge Python.
I think a lot of the criticisms so far are that it's early days for Swift in ML, and that's one point the author is emphasizing.
It's not an inherent part of Swift the language, and efforts like those at Google and the open source development of Swift can develop more modern and suitable replacements for those libraries.
I like python, but man I really don't want to make large systems in it. Swift is a great language, and imo the biggest thing holding it back is that it's intertwined with a lot of Apple code. But it doesn't need to stay that way, and for that reason I applaud the efforts to move it beyond just an "app creation" language.
In a lot of content that teaches machine learning/AI, the linear algebra substrate of it is given short shrift.
The consumers of that content infer that the backing mathematics is easy or unimportant, while the creators of the content are not actually implying that, but just want to move on to what the audience came for.
I'm not an expert in machine learning so I can't say whether you can get by without a strong understanding of linear algebra, but my intuition answer is no you can't. Beside that, I enjoy the math for its own sake so I'm happy to trudge through the textbooks.
And no, I don't think the blog post makes any claims that it is a subsitute for taking a course in linear algebra.
Now that the ideas of things like vector spaces, norms, orthogonality, rank, basis, etc are nearly second nature, the concepts are useful as I study other branches of math which would feel impenetrable otherwise.
YMMV, and if you can learn from condensed materials go for it, but I might be too dumb for it work lol. I think the real benefit accrues to the author who had to work out how to teach these concepts to others.
You do not explicitly state whether you created this software on company time or with company equipment. If you did it with personal equipment, I'd try to sell it to them. If that's unworkable, you could give the software to them and then offer to be their consultant to keep it working and to add features, at a hefty salary bump.
Ultimately we need to know what you are trying to optimize for: positioning in the company hierarchy, money, ...?
What is the underlying problem you're referring to?
It is admittedly a rabbit hole when you're writing code to debug code. But isn't that the whole spirit of writing tools to make your life easier and more efficient? We pile new abstraction layers atop more and more abstraction layers, and then we get computing in its impressive form as it exists today.
GDB and LLDB and any other debugger are huge software libraries for debugging, and yeah they might introduce bugs as well, but that does that mean they shouldn't be used or that they're not useful? I find it quite useful that LLDB has a scripting interface to automate debug sequences I find myself doing over and over. And since there is a scripting interface, we can find open source libraries for common debug tasks so when there is a bug, the bug is shallow due to many eyes.
Debugging a Debugger
Debugging a Debugger
If you are going to verify over SMS, don't have your SMS messages forwarded to email as that would render your 2fa pointless.
http://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-...
With that said, I have little reason to use R right now except for it's excellent plotting ability with ggplot2. Otherwise for data munging, wrangling, connecting to databases, doing unit testing, etc - R is a giant PITA. Better to stick to Python for that. And as I learn D3, I think I'll use R even less for visualization.
Therefore R will only be valuable to me once I can harness it's power for data mining and machine learning, which is it's killer feature, IMHO.
If I need to search for news, images, etc then it's back to Google. For me it's akin to using the command line for most tasks, but every so often opening up a cluttered but easier to use GUI.