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Thread Rules 1. This is not a "do my homework for me" thread. If you have specific questions, ask, but don't post an assignment or homework problem and expect an exact solution. 2. No recruiting for your cockamamie projects (you won't replace facebook with 3 dudes you found on the internet and $20) 3. If you can't articulate why a language is bad, don't start slinging shit about it. Just remember that nothing is worse than making CSS IE6 compatible. 4. Use [code] tags to format code blocks. | ||
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Thaniri
1264 Posts
I was more viewing both sides of the coin. The thread really did look like one dude saying college is useless and one dude saying cs logic is essential. I'll bow out because it's not a hill I am willing to die on. The smartest guy I ever worked with doesn't have a highschool diploma. However he is 1 guy and all of my other smart coworkers have degrees. Statistics would bear out that the degree is useful in most people's eyes in that regard. | ||
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Deleted User 3420
24492 Posts
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Deleted User 3420
24492 Posts
#1: http://web.utk.edu/~wfeng1/spark/_images/fnn.jpg #2: https://c7.uihere.com/files/261/538/457/artificial-neural-network-deep-learning-convolutional-neural-network-backpropagation-feedforward-neural-network-neural.jpg In image 1, earlier outputs are feeding into the bias units of the later layers. In image 2, this is not the case. I am guessing that image #2 is incorrect? Bias nodes are just a constant value for purposes of making linear algebra work, right? | ||
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SC-Shield
Bulgaria832 Posts
C++11
Also, a few other C++ details. I guess during first reading I was focused on learning new features, and now I can appreciate all the small details.  Edit: It's funny how the C++ committee forgot to add std::cbegin(), std::cend(), std::crbegin() and std::crend() in C++11. | ||
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berated-
United States1134 Posts
On March 02 2019 14:14 Thaniri wrote: What forums/blogs/websites/books do you guys read for system design topics? I just realized that when I have a random question on how best to design a system, I don't actually have a place to post and discuss or search for an answer. For example, the one that just came to mind: How do people loadbalance web application front ends in the cloud? Traditionally, we put a webserver in front of of a bunch of webapps and just scale the number of webapps as required. However, with the magic of auto scaling groups, in order to do that, you need to make a config change to your balancer manager every time a new web app server gets brought up or deleted. So due to the magic of the cloud we can do things like ALBs in AWS. But does that mean we no longer use nginx/apache? All the configs like reverse proxy rules, ip blocking configs, and SSL certs can't be expressed in AWS ELB rules. And doing something like a letsencrypt cert renewal would be hard for an ELB. You'd need to somehow do the renewal on a client, run a terraform/cloudformation template that takes the new cert from the client and put it on the server. The most idiot proof thing I can think of is to put a webserver onto every server with a webapp running. Put that server type into an autoscaling group, and delegate ALL loadbalancing to the ELB. That way any complex webserver configs are retained. The obvious issue with this is that you will end up with a silly number of webservers in a given deployment. Also a valid answer to this would be "go learn kubernetes for amazing autoscaling and self healing you caveman" but I'm a luddite who still uses bare metal. And not enough bare metal to make a borg cluster  When you find an answer of where to go, please let everyone know, including me. Some people pay big $$$ for that information. Your proposal is pretty much what we are doing. We run apache servers on every node of our webapps to retain the things like you are talking about. We run java webapps, and the apache along side of it. The overhead is basically negligible, so yeah.. you have an extra process on all your nodes, but you were going to have that number of nodes anyways. Load balancing and TLS termination is done at the LB level. I'm more on the dev side, but, on prem cert management is a pain in the ass. I've heard doing so in amazon is super simple. Even if you had let's encrypt going, managing certs on a top level LB is easier than doing so on each apache. That being said... if what you are doing is currently not working - then imho, yes, kubernetes is the future. The learning curve is massive, building it yourself is frowned upon (by some) ...but the abstractions and abilities are incredible. If you're already in the cloud, then using someone to manage the cluster for you is ideal. | ||
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sabas123
Netherlands3122 Posts
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Neneu
Norway492 Posts
On March 04 2019 03:05 travis wrote: Hey so I was looking at images of feedforward neural network and I see two different things come up: #1: http://web.utk.edu/~wfeng1/spark/_images/fnn.jpg #2: https://c7.uihere.com/files/261/538/457/artificial-neural-network-deep-learning-convolutional-neural-network-backpropagation-feedforward-neural-network-neural.jpg In image 1, earlier outputs are feeding into the bias units of the later layers. In image 2, this is not the case. I am guessing that image #2 is incorrect? Bias nodes are just a constant value for purposes of making linear algebra work, right? Slightly depends on the problem you are supposed to solve, but the general idea is that the main function of a bias is to provide every node with a constant value which can be trained. This can be achieved by a single bias node with connections to N nodes or with N bias nodes each with a single connection, the result should generally be the same. So even if in image two they only have one bias node (i = 0) per layer, it should work as well as in image one. But yeah, not a big fan of how they did the diagram. | ||
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ZenithM
France15952 Posts
On March 07 2019 19:47 sabas123 wrote: Hey travis, how did your quest for solving that NP problem go? That's the topic that makes me single-handedly stop checking on this thread for 3 months. | ||
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Deleted User 3420
24492 Posts
sabas: I ended up learning a lot, particularly about hamiltonian cycles. Made some interesting heuristics that work some of the time even for decently large n. I want to keep investigating but I have to do school stuff and it's just so time consuming.... like incredibly time consuming to do any detailed exploration of the behavior of these graphs and how they evolve with more nodes or edges added. | ||
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Manit0u
Poland17450 Posts
https://github.com/koudelka/visualixir | ||
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ZenithM
France15952 Posts
Granted this time it wasn't your fault, you were prompted to give us an update. | ||
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WarSame
Canada1950 Posts
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ZenithM
France15952 Posts
Any CS professor would have told him to focus on something else than P=NP (especially this particular side of the P vs NP question...). It's really a testament to this thread's welcoming atmosphere that there is only one guy making fun of another for working on P vs NP. It's impressive and I'm a bit ashamed to be that guy now but I also don't dominate the thread with snarky posts either. Anyway, I won't post again on that topic if it comes up, even if it's 3 months from now . Consider me self-banned from doing that. | ||
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solidbebe
Netherlands4921 Posts
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enigmaticcam
United States280 Posts
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Excludos
Norway8195 Posts
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Chocolate
United States2350 Posts
On March 04 2019 03:05 travis wrote: Hey so I was looking at images of feedforward neural network and I see two different things come up: #1: http://web.utk.edu/~wfeng1/spark/_images/fnn.jpg #2: https://c7.uihere.com/files/261/538/457/artificial-neural-network-deep-learning-convolutional-neural-network-backpropagation-feedforward-neural-network-neural.jpg In image 1, earlier outputs are feeding into the bias units of the later layers. In image 2, this is not the case. I am guessing that image #2 is incorrect? Bias nodes are just a constant value for purposes of making linear algebra work, right? I prefer not to think of a FFNN with "bias units". FFNN are essentially multilayer multi-perception networks. In the perceptron model each component of a layer is a perceptron consisting of a weight for each input and a bias. So both these pictures are just different ways of representing bias. I actually think the first picture is the worse because it separates out the bias from the nodes. The second picture more clearly conveys that each node has a bias value. | ||
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Chocolate
United States2350 Posts
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Neshapotamus
United States163 Posts
On March 04 2019 03:05 travis wrote: Hey so I was looking at images of feedforward neural network and I see two different things come up: #1: http://web.utk.edu/~wfeng1/spark/_images/fnn.jpg #2: https://c7.uihere.com/files/261/538/457/artificial-neural-network-deep-learning-convolutional-neural-network-backpropagation-feedforward-neural-network-neural.jpg In image 1, earlier outputs are feeding into the bias units of the later layers. In image 2, this is not the case. I am guessing that image #2 is incorrect? Bias nodes are just a constant value for purposes of making linear algebra work, right? The geometric interpretation of the bias is the constant value that lets you shift your graph vertically. Technically, this shouldn't be multiplied by any value in the previous layers unless you want to have the property that the function goes through the origin. ex: y=w1*z1+w2*z2+..w_n*z_n+b | ||
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SC-Shield
Bulgaria832 Posts
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