Activation Functions - EXPLAINED!

[@desalefentaw8658]

wow, one of the best highlights of activation functions on the internet. Thank you for doing this video

[@ranchokastudent1473]

This video has been immensely helpful! i was struggling with grasping the concept of activation functions but thanks to your video I was able to understand it within minutes. Thanks a lot!

[@GauravSharma-ui4yd]

Awesome as always. Some points to ponder correct me if I am wrong 1. Relu is just not a activation but can also be thought as a self regularizer, as it offs all those neurones whose values are negative, so it's just a kind of automatic dropout. 2. A neutral net with just input and output layer, with softmax at the output layer is logistic regression, but when we add hidden layers in this network with no hidden activations then it's more Powerful than just vanilla logistic regression as it is now taking linear combination of linear combinations with different weight settings. But it still results in linear boundaries. Lastly your contributions to the community is very valuable, clears a lot nitty-gritty details in short time. Keep going like this :)

[@generichuman_]

No, dropout is different. Random sets of neurons are turned off in order to cause the neurons to form redundancies which can make the model more robust. In the case of dying Relu, the same neurons are always dead, making them useless. Dropout is desirable and deliberate, dying Relu is not.

[@linuxbrad]

7:48 "once it hits zero the neuron becomes useless and there is no learning" this explains so much, thank you!

[@UdemmyUdemmy]

the screetching noise is irrtitaing..else nice tutoial

[@prakharmishra-m5r]

I agree

[@malikkashifsaeed1938]

agreed. cringe and irritating

[@semon00]

I don't agree

[@otabeknajimov9697]

best explanation of activation functions I ever seen

[@lex8799]

Very clear, thanks bro, you’re heaven sent !

[@jhondavidson2049]

I'm learning deep learning rn and using the deep learning book published by MIT press for the same. That's kinda complicated for me to understand especially these parts cause m still an undergrad and have 0 previous experience with this. Thank you for explaining this so well.

[@CodeEmporium]

Anytime :)

[@SkittlesWrap]

Straight to the point. Nice and super clean explanation for non-linear activation functions. Thanks!

[@x_ma_ryu_x]

Thanks for the tutorial. I found the noises very cringe.

[@PrymeOrigin]

One of the best explanations ive come across

[@adrianharo6586]

Great video! The dissapointed gestures were a bit too much x'D A question I did have as a beginner was. What does it mean for a sigmoid gradient to "squeeze" values, as in they become smaller and smaller as they back propagate?

[@AnkityadavGrowConscious]

It means that sigmoid function will always output a value between 0 and 1 regardless of any real number input. Notice the mathematical formula and graph of a sigmoid function for better clarity. Any real number will be converted to a number between 0 and 1. Hence sigmoid is said to "squeeze" values.

[@deepaksingh9318]

Wow... Perfect and easiest way to explain it.. Everyone talks about what activations do but nobody shows in how actually it looks like behind the algos.. And you explain things in the most easiest way which are so easy to understand and remember.. So a big like for. All your videos.. Could uh make more and more and DL.. 😄

[@CodeEmporium]

Thank you. I'm always thinking of more content :)

[@linuxbrad]

9:03 what do you mean "most neurons are off during the forward step"?

[@epiccabbage6530]

What are the axises on these graphs? Is it inputs, input*weights + bias for linear?

[@NITHIN-tu7qo]

did you get answer for it?

[@fahadmehfooz6970]

Amazing! Finally I am able to visualise vanishing gradient descent and dying relu.

[@CodeEmporium]

Glad!

[@PritishMishra]

The most thing I love about your videos is the fun you add... Learning becomes a bit easier

[@nguyenngocly1484]

With ReLU f(x)=x is connect, f(x)=0 is disconnect. A ReLU net is a switched system of dot products, if that means anything to you.

[@AmirhosseinKhademi-in6gs]

but we cannot use ReLU for the regression of functions with high degrees of derivatives! In that case, we should still go with infinitely differentiable activation functions like "Tanh", right?

[@oheldad]

Great video ! And what is more great - are the useful references you add at the description. ( For me (1)+(7) answer the questions I asked my self at the end of your video - so its was on point ) ! Thank you !

[@CodeEmporium]

Haha. Glad the references are useful! :)

[@deepakkota6672]

Wooo, Did I just noticed the complex explained simple. Thanks! Looking forward to more videos.

[@kanehooper00]

Excellent job. There is way too much "mysticism" around neural networks. This shows clearly that for a classification problem all the nerual net is doing is creating a boundary function. Of course it gets complicated in multiple dimensions. But your explanations and use of graphs is excellent

[@wucga9335]

so how do we know when to use relu or leacky relu? do we just use leacky relu all together in all cases?

[@dazzykin]

Can you cover tanh activation? (Thanks for making this one so good!)

[@CodeEmporium]

I wonder if there is enough support that warrants a video on just tanh. Will look into it though! And thanks for the compliments :)

[@AymaneArfaoui]

what does x and y represent in the graph you use to show the cats and dog points ?

[@TheAscent_]

@6:24 How does passing what is a straight line into the softmax function also give us a straight line? Isn't the output, and consequently the decision boundary, a sigmoid? Or is it the output before passing it into the activation function what counts as the decision boundary?

[@CodeEmporium]

6:45 - The line corresponds to those points in the feature space (the 2 feature values) where The sigmoid's height is 0.5.

[@vasudhatapriya6315]

How is softmax a linear function here? Shouldn't it be non linear?

[@shivendunsahi]

I discovered your page just yesterday and might I say, YOU'RE AWESOME! Thanks for such good content bro.

[@CodeEmporium]

Thanks homie! Will dish out more soon!

[@alonsomartinez9588]

Awesome vid! Small sug: I might check the volume levels, during the screaming in :56 it was a bit painful to my ear and possibly sounded like audio clipping.

[@the-tankeur1982]

I hate you for making that noises, i want to learn, comedia is something i would pass on

[@prakharrai1090]

can we use linear activation with hinge loss for Linear svm for binary classification.

[@Nathouuuutheone]

What decides the shape of the boundary?

[@rishabhmishra279]

Great explanation ! and the animations with maths formula and visualizing it is awesome !! Many thanks !

[@kphk3428]

1:16 I couldn't see that there were different colors so I was confused. Also I found the voicing of the training neural net annoying. But some people may like what other people dislike, so it's up to you to keep on voicing them.

[@gabe8168]

the dude is making these videos alone, if you don't like his voice that's on you, but he can't just change his voice

[@mikewang8368]

better than most professors, thanks for great video

[@CodeEmporium]

Thanks!!

[@fazlayrabby3709]

Can you please explain this "No gradient means no learning"?

[@mitchfrtube]

Gradient is in Charge of updating the weights in backpropagation process. If the gradient is zero = no gradient.. no update will Take place.. so.. the learning process stops. You Will add Zero to the weights and they Will be stucked remaining the Same.

[@shrikanthnc3664]

Great explanation! Had to switch to earphones though :P

[@MrGarg10may]

then why isn't leaky RELU ELU used everywhere in LSTM, GRU, Transformers ..? why is RELU used everywhere

[@DrparadoxDrparadox]

Great Video. Could you explain what U and V are equal to in this equation : o = Ux + V ? And How did you come up with the decision boundary equation and how did you determine the values of w1 and w2 ? Thanks in advance

[@Mohammed-rx6ok]

Amazing explanation and also funny 😅👏👏👏

[@ronin6158]

it should be possible to let (part of) the net optimize its own activation function no?

[@tarkatirtha]

Lovely intro! I am learning at the age of 58!

[@programmer4047]

So, we should always use leaky reLU

[@superghettoindian01]

Another great video 🎉🎉🎉!

[@CodeEmporium]

Thanks so much!

[@RJYL]

Great explanation for activation function I like it so much

[@CodeEmporium]

Thanks so much for commenting

[@rasikannanl3476]

great .. so many thanks ... need more explanation

[@pouyan74]

I've read at least three books on ANN's so far, but it's only now, after watching this video, that I have the intuition of what exactly is going on and how do activation functions break linearity!

[@alifia276]

Thank you for sharing! This video cleared my doubts and gave me a good introduction to learn further

[@CodeEmporium]

Super glad :)

[@ShivamPanchbhai]

this guy is genius

[@eeera-op8vw]

good explanation for a beginner

[@sgrimm7346]

Excellent explanation. Thank you.

[@cheseremtitus1501]

Amazing presentation ,easy and captivating to grasp

[@CodeEmporium]

Glad you liked it! Thank you!

[@splytrz]

I've been trying to make a convolutional autoencoder for mnist, and at first I used sigmoid activation on the convolutional part and it couldn't make anything better than just a black screen on the output but when I removed all activation functions it worked well. Does anyone have any idea why that happened?

[@fatgnome]

Are the outputs properly scaled back to pixel values after being squeezed by sigmoid?

[@splytrz]

@@fatgnome Yes. Otherwise the output wouldn't match with images. Also I checked model.summary() every time I made changes to the model.

[@malekaburaddaha5910]

Thank you very much for the great, and smooth explanation. This was really perfect.

[@CodeEmporium]

Much appreciated Malek! Thanks for watching!

[@najinajari3531]

Great Video and great page :) Which softwares you use to make these videos ?

[@CodeEmporium]

Thanks! I use Camtasia Studio for the editing; Photoshop and draw.io for the images.

[@blackswann9555]

excellent explaination

[@phucphan4195]

thank you very much, this is really helpful

[@CodeEmporium]

Thanks:)

[@wagsman9999]

Beautiful explanation!

[@fredrikt6980]

Great explanation. Just add more contrast to you color selection.

[@CodeEmporium]

My palette is rather bland i admit

[@VinVin21969]

plot twist: its not that the boundary no longer changes, the vanishing gradient cause the gradient to be very small , that we can assume it is negligible

[@CodeEmporium]

Danana nanana nanana nana

[@meghnasingh9941]

wow, that was really helpful, thanks a ton!!!!

[@CodeEmporium]

Glad to hear that. Thanks for watching!

[@yachen6562]

Really awesome video!

[@SeloniSinha]

wonderful explanation!!!

[@mohammadsaqibshah9252]

This was an amazing video!!! Keep up the good work!

[@CodeEmporium]

Thanks so much!

[@younus6133]

oh man, amazing explanation.Thanks

[@bartekdurczak4085]

good explanation but the noises are little bit annoying but thank you bro

[@keanuhero303]

What's the +1 node on each layer?

[@avijain6277]

The bias term

[@myrondcunha5670]

THIS HELPED SO MUCH! THANK YOU!

[@simranjoharle4220]

This was really helpful! Thanks!

[@CodeEmporium]

Thanks for watching :)

[@kellaerictech]

That's great explanation

[@CodeEmporium]

Thanks so much for watching !

[@prashantk3088]

really helpful..thanks

[@youssofhammoud6335]

What I was looking for. Thanks!

[@mangaenfrancais934]

Great video, keep going !

[@yukuchan]

nice video ♥

[@tahirali959]

good work bro keep it up -

[@CodeEmporium]

Will do homie

[@Edu888777]

I still dont understand what a activation function is

[@aaryamansharma6805]

awesome video

[@ankitganeshpurkar]

Nicely explained

[@CodeEmporium]

Thanks for watching this too

[@LifeKiT-i]

With graphical calculator, your explanation is sanely clear!! thank you!!

[@CodeEmporium]

Thanks so much for the kind comment! Glad the strategy of explaining is useful :)

[@jigarshah1883]

Awesome video man !

[@undisclosedmusic4969]

Swish: activation function. Swift: programming language. More homework, less sound effects 😀

[@CodeEmporium]

Nice catch. I misspoke :)

[@jamesdunbar2386]

Quality video!

[@ExMuslimProphetMuhammad]

Bhai video shayad accha hoga but thumbnail pe Teri pic dekhke hi kafi log click na kare, I'm here just to let you know this:avoid putting your face on thumbnail or in video as no one is interested in seeing the educator while watching technical videos.

[@CodeEmporium]

You clicked. That's all i care about ;)

[@masthanjinostra2981]

Benefited a lot

[@CodeEmporium]

Awesome! Glad!

[@abd0ulz942]

learn Activation Functions with Dora but I honestly it is good

[@harishp6611]

yes! I liked it. Keep it up.

[@jaheerkalanthar816]

Thanks mate

[@ehsankhorasani_]

good job thank you

[@CodeEmporium]

Very welcome!

[@patite3103]

Amazing!

[@ShinsekaiAcademy]

thanks my man.

[@CodeEmporium]

You are oh so welcome

[@igorpostoev2077]

Thanks man)

[@farhanfadhilah5247]

this is helpful, thanks :)

[@francycharuto]

gold, gold, gold.

[@uzairkhan7430]

awesome

[@히안-p3j]

I don't understand..

[@Acampandoconfrikis]

thanks brah

[@jhondavidson2049]

Amazing!!!!!!!!!!!!!!!!!

[@CodeEmporium]

Thanks!!!!!!!!!!

[@abdussametturker]

thx. subscribed

[@wadyn95]

Wtf what's the sound of pictures...

[@zaidalattar2483]

Perfect explanation!... Thanks

[@CodeEmporium]

Much appreciated!