#linearled

[necarion]

You also have the thermodynamic 1/T scale, where absolute zero is infinity, total entropy (infinitely hot) is +0, and then proceeds smoothly into the negative temperatures starting from too-ordered total entropy (-0) down to - inf.

The rule is then "an object will always give energy to something at a higher temperature".

You can linearlize the scale around human temperatures to make something useful for us, but it becomes very inconvenient for super hot things. Also super cold ones, although there at least it does help give a sense of how hard it is to get colder.

There is no single “best” temperature scale—which one is most useful depends on context and culture—but there is absolutely a worst one: the Delisle scale, where degrees go down instead of up and zero Kelvin is like 560 degrees.

[allen-linearledstriplights]

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[greenlux-led]

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[sagaciouscejai]

ive said before that pikmin 4 leaning in on pikmin 2 as inspo felt a bit, like, surface level fanservice-y at first but honestly the level design creativity of course gets so much better in the later areas and stages so i think its nintendo difficulty scaling blunting my opinion of it at first. Still, the limitations early game are pretty engaging so its hardly much of a fault that the relative non-linearlity of the caves make them a bit boring until you hit the later areas... though submerged didnt need to be made THAT easy, did it?

I'm unsure if Pikmin 4 is the best Pikmin game but its so solid so far.

[ada-ledluz-blog]

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[jacob-cs]

MIT RES.6-012 Introduction to Probability 5강

L05.2 Definition of Random Variables https://youtu.be/vfqPpai_9jI

random variable 개념 잡기 참고 영상

https://youtu.be/3v9w79NhsfI

실제 상황에��� 동전을 던진 경우 head, tail이 나올수 있는데 이 각각의 경우를 1, 0으로 대응 시켜 주는 함수가 random variable이다.  주사위를 던져서 나온 값들의 합이렇게 기술하는 것보다 이에 대응하는 값을 만들어 주는 random variable Y를 이용하는 것이 더 깔끔한 것을 알수 있다. 

L05.3 Probability Mass Functions https://youtu.be/zW1_iugJvF0

L05.4 Bernoulli & Indicator Random Variables https://youtu.be/J8L9kRGSvSY

L05.5 Uniform Random Variables https://youtu.be/JoQDJMZA7F8

L05.6 Binomial Random Variables https://youtu.be/jOC4ATKBWlI

L05.7 Geometric Random Variables https://youtu.be/whbKmwMmB4s

L05.8 Expectation https://youtu.be/_yJsO5955ZE

indicator function에 대하여

https://youtu.be/V3pnr5gmJC8

L05.9 Elementary Properties of Expectation https://youtu.be/GARQ31BrKQA

L05.10 The Expected Value Rule https://youtu.be/gB5TCCfF6e4

L05.11 Linearity of Expectations https://youtu.be/0IJFBMIU6x4

맨 하단의 파란색으로 써있는 내용은 오직 linear 연산에만 적용된다.

[with-the-same-tattoos]

last anons ask could have been more constructive so I'll balance it with something nice! your comics may be hard to follow at times but the flow you have with your artwork and pacing can also be a great strength! perhaps using a grid structure to draw over will help you place things in a more linear way <3

AHHHH NOOOOO IT'S OK I TOTALLY GET WHAT THEY MEAN !!!! But thank you so much too, I'm always happy 2 hear what i can improve on o//w//o i'll try grid rulers / papers for that tysm for the idea :0c i hav a v simple idea for a brahms & loud comic thatd actually be perfect to practice linearlity so.... gucci

[antonchen2015]

View this post on Instagram

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[livingwellworld]

LED High Bay Warehouse Lighting 2 ft. Linear White Dimmable Daylight Hardwired Act at Once https://ebay.to/2JR74eb

[furiousfinnstan]

Like I'm aware that to change my behavior and keep my mental illnesses under control I'll have to be pushing myself and picking myself up(because progress isn't linearl and bad time periods will come and go)but on the other hand I'm so fucking tired I just wanna wake up and to be completely fine one day

[k00283645]

Project title: Bird Brain

My work investigates childhood development looking into who and why we trust. In adolescents who we trust is heavily influenced by the psychological process of imprinting. Imprinting is the learned habitual trust of another thing, that forms from repeated exposure to the thing, as a survival mechanism. Although there is no guarantee that what one imprints on is actually helpful let alone living. I use imagery of chicks as a familiar entry point to the psychology of Imprinting. The playful, colourful and simplistic Images combined with the stark linearl compositions aim to represent the blind trust of imprinting. The relief and screen printing oppose the aesthetics of etching techniques to further this idea. Imprinting and trust developed in childhood follows you throughout life, while it is a virtue to trust it can be easily taken advantage of.

[allen-linearledstriplights]

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[noitaicossid]

Healing from someone you once loved is like decluttering your apartment. It’s not an linearl and easy process. After you throw away most of the things, you think that you are done. But for the next month’s or maybe even years you are finding those little things. Somewhere in the corner, small needle is waiting to pinch you in your toe.

[ada-ledluz-blog]

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[oswednesday]

i wish control was like Super linearl I’m in gameplay terms and all the quests opened up in post game, like main quest getting to story point story point then the game opening up

[eurekakinginc]

"[D] How does neural network mimic non-linear feature-relations."- Detail: Hi all,Probably some silly questions. Please help me understand the below three questions:1.) How does neural network learn non-linear in terms of their features.Eg: if x>2 and y<3: then z, where x,y,z are input features to the network.Also how does it learn non-linearity in terms of multiplication and power of features. One way I can think of is learning the weighted sum of the log of the features and then using some exponential activation.The reason I am asking is that all the network does is matrix multiplication with the features.I agree that we have non-linear activation functions, but I am not able to understand how they learn non-linearity in terms of features.2.) ReLu seems to be mostly linear, how does it learn non-linearity.3.) Also, how does sigmoid learn non-linearlity withing features. Eg: Consider 2 layer neural network with both activations as sigmoid, then output can be written as ~ e^(w1*e^(w2*x+w3*y) + w4*e^(w5*x+w6*y)). This still doesn't seem to learn something like x*y or non_linear(x,y)​Looking for some detailed answers to above 3 questions. Similar post: https://www.reddit.com/r/MachineLearning/comments/5bq2ph/d_goodfellow_says_neural_nets_are_linear/​NOTE: Question is not about non-linearity of weights.​​Thanks !!. Caption by WillingCucumber. Posted By: www.eurekaking.com