Neural Network Architectures & Deep Learning

This video describes the variety of neural network architectures available to solve various problems in science ad engineering. Examples include convolutional neural networks (CNNs), recurrent neural networks (RNNs), and autoencoders.
Book website: databookuw.com/
Steve Brunton's website: eigensteve.com
Follow updates on Twitter @eigensteve
This video is part of a playlist "Intro to Data Science":
• Intro to Data Science
This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company

[@mickmickymick6927]

Does anyone else feel weird when he says Thank You at the end? He just gave me a free, high-quality, understandable lecture on neural networks. Man, thank *you*!

[@Eigensteve]

:) People watching and enjoying these videos makes it so much more fun to make them. So indeed, thanks for watching!

[@antoniofirenze]

@@Eigensteve ..being happy to see other people making progress. Man, you have a great heart..!

[@carol-lo]

Steve, we should be thanking "you"

[@oncedidactic]

Presenter with true class 👏

[@Learner..]

😁😍

[@teslamotorsx]

KZbin's recommendation algorithm is becoming self-aware...

[@florisr9]

It was KZbin's turn in the introduction round

[@GowthamRaghavanR]

I hope Jus relu and sigmoid

[@Xaminn]

@@GowthamRaghavanR those are the safe ones

[@resinsmp]

Imagine for a second also what the algorithm never recommended to you, because it already knew you were aware.

[@Xaminn]

@@resinsmp Now that's an interesting thought haha. "Since user searched this type of topic, it must already be aware of some other certain type of topics." Simply marvelous!

[@farabor7382]

I don't know why youtube decided I needed that little course, but I'm glad that it did now.

[@brockborrmann2931]

This video has common variables with other videos you watch!

[@TonyGiannetti]

Sounds like you’ve been autoencoded

[@fitokay]

That's why the CF algorithm did

[@Kucherenko90]

same thing

[@user-yp6ze3dh5j]

KZbin also uses neural networks

[@Savedbygrace952]

I am addicted to your series of lectures for the last three months. your "welcome back" intro looks like a chorus to me. thank you!

[@johnwilson4909]

Steve, you are the first person I have ever seen describe an overview of neural networks without paralyzing the consciousness of the average person. I look forward to more of your lectures, focused in depth on particular aspects of deep learning. It is not hard to get an AI toolkit for experimentation. It is hard to get a toolkit and know what to do with it. My personal interest is in NLR (natural language recognition) and NLP (natural language programming) as applied to formal language sources such as dictionaries and encyclopedias. I look forward to lectures covering extant NLP AI toolkits. Sincerely, John

[@pb25193]

John, I recommend Stanford's course on recurrent neural networks. Free on KZbin. It's a playlist with over 20 lectures

[@pb25193]

kzbin.info/aero/PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z

[@theunityofthejust-justifyi7951]

You really simplify the stuff in a way that has me feel enthusiastic to learn it. Thank you.

[@PhoebeJCPSkunccMDsImagitorium]

steve brunton idk who u r before watching this. but this presentation style of a glass whiteboard w/ image superimposed is the best way ive ever seen someone teach tbh. thank u at least for that. but more importantly this actually helped me understand the beast of neural nets a little more and hopefully be more prepared when our new ai overlords enslave us at least we will know how they think

[@elverman]

This is the best short intro to this topic I've seen. Thanks!

[@brian_c_park]

Thank you, I've always seen the term neural networks generalized and always thought of it as probably a bunch of matrix operations. But now I know that there are diverse variations and use cases for them

[@dantescanline]

This was massively helpful as an intro! When my question is just "yes but how does this ACTUALLY work", you either get pointlessly high level metaphors about it being like your brain, or jumping straight into gradient descent and all the math behind training. A+ video, thanks.

[@chris_jorge]

forget neural networks, this guy figured out that it's better if you stand behind what your presenting instead of in front of it. mind blown

[@MikaelMurstam]

Very nice. I like the autoencoders. That is basically just understanding. Intelligence is basically just a compression algorithm. The more you understand the less data you have to save. You can extract information from your understanding. That's basically what the autoencoder is about. For instance, if you want to save an image of a circle you can store all the pixels in the image, or store the radius, position and color of it. Which one takes up more space? Well, storing the pixels. We can use our understanding of the image containing a circle in order to compress it. Our understanding IS the compression. The compression IS the understanding. It's the same.

[@TheMagicmagic290]

shut up

[@dizzydtv]

profound observation

[@bdi_vd3677]

Thank you for your comment, excellent observance!

[@SirTravelMuffin]

I dig that perspective. I do think that compression can have some downsides. I feel like my emotional reactions to things are a sort of "compression". I can't keep track of everything I've read about a potentially political topic, but I can remember how it made me feel.

[@PerfectlyNormalBeast]

I like to think of autoencoder as an architect outputting a blueprint, then a construction company building that building

[@Jorpl_]

Hey I just wanted to say thank you for making this video. I found it really helpful! I particularly enjoyed your presentation format, and the digestible length. About to watch a whole bunch more of you videos! :)

[@KeenyNewton]

These were most productive 9 minutes. Great explanation on the architectures.

[@culperat]

Important note about the function operating on a node. If the functions of two adjacent layers are linear, then they can be equivalently represented as a single layer (compositions of linear transforms is itself a linear transformation and thus could just be its own layer). So, nonlinear transformations are -necessary- for deep networks (not just neural networks). That isn't to say you can't have a composition of linear transformations to compose an overall linear transformation, if there's nonlinear constraints for each operator.

[@ArneBab]

Thank you for your video! Seeing your example for principal values decomposition made neural networks much clearer to me than anything else I had seen till now. It allowed me to connect this to SVD-based linear modeling I used almost 10 years ago to create simplified models of visual features seen in fluid dynamics. I did not expect how much easier this suddenly seemed when it connected to what I already knew.

[@-SUM1-]

KZbin is trying to teach us about itself.

[@FriendlyPerson-zb4gv]

Hahaha. Good.

[@ImaginaryMdA]

It's becoming sentient! Even worse, it's a teenager who just wants to be understood. XD

[@amegatron07]

I started to learn NNs in good old early 2000-s. No internet, no collegues, nor even friends to share my excitement about NNs. But even then it was obvious that the future lies with them, though I had to concentrate on more essential skills for my living. And only now, after so many years have passed, I tend to come back to NNs, cause I'm still very excited about them and it is much-much-much easier now at least ot play with them (much more powerful computers, extensive online knowlegde base, community, whatever), not speaking about career opportunities. I'm glad YT somehow guessed I'm interested in NNs, though I haven't yet searched for it AFAIR. It gives me another impetus to start learning them again. Thanks for the video! Liked and sub-ed.

[@PiercingSight]

This is a perfectly compressed overview of neural networks. What autoencoder did you use to write this?

[@bunderbah]

Human brain

[@MilaPronto]

@@bunderbah Bruman hain

[@3snoW_]

@@MilaPronto Humain bran

[@mbonuchinedu2420]

one hot encoder. lols

[@mjafar]

@@mbonuchinedu2420 That's like a robot trying to be funny

[@tottiegod8021]

Great content for existing developers. Wow. Incredible. To say the least I am speechless. You didn’t waste my time and I appreciate that!!

[@lucasb.2410]

Amazing video and explication , focusing on key points is very interesting for such sciences, thank you a lot and keep doing that !

[@josephyoung6749]

Amazing program... I love the thing he's drawing on that projects his diagrams.

[@RolandoLopezNieto]

I just found your channel as a suggestion from a 3Blue1Brown video. I subscribed instantly, easily explained, thanks.

[@Eigensteve]

So cool! Which video?

[@robertschlesinger1342]

Excellent overview on neural network architecture. Very interesting and worthwhile video.

[@XecutionStyle]

Sir your deep learning videos are the only ones on KZbin I take seriously.

[@husane2161]

Awesome concise high level explanation! Thank you

[@parvezshahamed370]

I have been looking for this content a really long time. Thanks so much.

[@easylearn9350]

Simple perfect enjoyable expaining of DNNs. Thanks for sharing!

[@lightspeedlion]

Amazing time spent to understand the Networks a little more.

[@YASHSHARMA-bf2mm]

Thank you so much for the video! The way you teach makes learning so much fun:) If you were born in ancient time, you alone would have shot the literacy rate by over 20%

[@nghetruyenradio]

Best. I love your lecture. It explains problem in a simple way. Thank you so much.

[@Illu07]

Gosh i needed this intro at the start of my seminar paper...

[@SaidakbarP]

Thank you for a good explanation. This is the quality of content we want to see! 10 folds better than Siraj Raval's channel, in my opinion.

[@fzigunov]

Well, that makes sense given he's a renowned professor =)

[@goodlack9093]

Love your videos and your book! Can't wait to start working through it actually!

[@VikiGradwohl]

A really really great video to point out essentials of Neural Network Architecture, thanks for that video

[@carnivalwrestler]

Clear and concise. Thanks for posting.

[@mrknarf4438]

Clear, simple, effective. Thank you!

[@mrknarf4438]

Also loved the graphic style. We're the images projected on a screen in front of you? Great result, I wish more people showed info this way

[@satoshinakamoto171]

thank you. i somehow get inspiration from videos like these.

[@jonacacarr3839]

This was most helpful, very clear, thank you

[@solargoldfish]

Great explanation. Thank you.

[@bambam10years]

Such a great explanation, thank you

[@beepboopgpt1439]

Thank you so much! I needed this.

[@myway2mars]

Great explanation. Thank you!

[@userou-ig1ze]

simply great, thanks for this intro video

[@raoofnaushad4318]

Thanks for sharing Steve

[@IamWillMatos]

Great work on this video!

[@karemabuowda2695]

Thank you very much for this extraordinary way of teaching.

[@tw0ey3dm4n]

Strangely enough. I needed this vid. Thank you YT ALGO

[@kevintacheny1211]

One of the best introductions to AI I have seen.

[@bensmith9253]

YES. ☝️this

[@alalalal5952]

ty YT, is all joy your latest state of recomendations

[@toonheylen4707]

Amazing video, thanks for the information

[@aminnima6145]

Thank you for this beautiful explanation.. I really enjoy it.

[@yourikhan4425]

I need to watch all the videos of this channel.

[@SimulationSeries]

Adore this free online schooling, thanks so much Steve!!

[@Eigensteve]

Glad you enjoy it! Thanks!

[@neiltucker1355]

a fantastic overview thanks!!♥

[@jaredbeckwith]

Good overall neural net explanation!

[@sitrakaforler8696]

Really clear. Thanks for the vidéo !

[@flaviudsi]

Very well explained. Thank you

[@FlowerPowered420]

I really appreciate this talk, thank you.

[@reallynotadatascientist]

"...a smiley face, I took this from Wikipedia." You know he's an academic when he cites EVERYTHING. He cites a smiley face image.

[@doctorshadow2482]

He Steve, thank you a lot for all your brilliant videos! One request on the topic, could you please cover how all this works with shift/rotation/scale of the image? Nobody on youtube covers this tricky part of the neuron networks used for image recognition. I keep fingers crossed that you the one who could clarify this.

[@AllTypeGaming6596]

So youtube know that i am currently learning neural network and this video is appear in my recommendation ,great

[@saysoy1]

once you get hold of the back propagation and how to do the chain rule derivatives, you understand that was not the goal! you merely opened the door, and this video is the way to your goal!

[@abhaythakur8572]

Thanks for this explanation

[@jimparsons6803]

Liked that the approach was direct and simplistic; and of course you can write your code in this manner too. So that you're not overwhelmed. Say four or five layers being coded, then you have outboard functions that handle the input and out put arrays. This last might take up most of the landscape of a program. Isn't this fellow clever? Dang. He's gotta be a Professor somewhere. Many thanks. The computer training that I had gotten was very rudimentary, first in the 60s and then another drop in the mid 90s. Luckily there's YT where you can catch up. And after a while the 'training' starts to remind you of subliminal sorts of stuff. Maybe?

[@ts.nathan7786]

Very good explanation. 🎉

[@youcanlearnallthethingstec1176]

I like the way of explaining by projecting on glass board....very very nice...

[@DanWilan]

Finally a good presentation

[@Eigensteve]

Thanks!

[@GarimaaThakur]

Glad I found this channel! Loved everything about this video.

[@Eigensteve]

Glad you enjoy it!

[@insomnia20422]

this is 9 minutes of pure quality education

[@latestcoder]

Ok, gotta bring my notebook, thank you for the content btw

[@kennjank9335]

One of the most effective and useful introductory lectures on neural networks you can attend. It provides basic terminology and enables a good foundation for other lectures. HIGHLY RECOMMENDED. It would be helpful, Mr. Bunton, to say a little bit more about Neurons. Is a neuron strictly a LOGICAL function point in a process (my simple excel cell doing a logical function qualifies as a neuron with your definition), is it a PHYSICAL function point like a server, or is it both? Was there a reason you did not mention restricted Boltzmann motors? Thank you again, Sir, for the quality of this lecture.

[@JorgeMartinez-xb2ks]

A neuron is pure software, a computational unit that mimics the basic functions of a biological neuron. While software relies on specific hardware for execution, a neuron is not a simple server. Unlike an Excel cell, which takes a single input and produces a straightforward output, a neuron receives multiple inputs from other neurons, processes them, and generates an output based on the combined information. Each input to a neuron is multiplied by a weight, a numerical value that represents the strength of the connection between the neurons. These weighted inputs are then summed together, and a bias value, representing an inherent offset, is added to the result. The resulting value is then passed through an activation function, which introduces non-linearity into the network's decision-making process. Activation functions, such as sigmoid and ReLU, transform the weighted input into the neuron's output, allowing the network to capture complex patterns and relationships in the data. ReLU is often used as an activation function because it requires less computational power compared to other activation functions, such as the sigmoid function. Through a process called learning, artificial neurons adjust their weights over time, enabling the network to improve its performance on a given task. Algorithms like back propagation guide this learning process, allowing the network to minimize errors and optimize its decision-making capabilities. Hope this helps.

[@Selbstzensur]

Thanks, this was awesome.

[@mikegunner5539]

That was beautiful.

[@izainonline]

Great explanation Thank u Sir

[@vijaykumar.jayaraj]

Very nice explanation

[@namhyeongtaek4653]

I love this man. You are my role model.

[@Eigensteve]

Thanks so much!

[@namhyeongtaek4653]

@@Eigensteve OMG it's my honor😯. I didn't expect you would read my comment lol. I hope I could get in to UW this fall so that I could be in your class in person.

[@randythamrin5976]

Amazing good explanation and simple word for non english native speaker like me

[@jeewonkyrapark9153]

Amazing. Thank you :)

[@its_me_kirankumar]

KZbin recommended it. But i love it.

[@lucyoriginales]

Awesome 😎... well ☺️ i didn’t understand much but i think I could use as inspiration to Spinal Cord my Dark Matter.

[@JohannesSchmitz]

Could you please do a follow up on this? I basically came here for the "many many more" you mentioned towards the end. LSTMs and other architectures that are useful for time series processing. It would be nice if you could do an overview video about that class of networks.

[@Didanihaaaa]

beautiful! thanks.

[@BenHutchison]

Oh wow I've been educated by your channel for a while now but did not realise you have published a textbook until your remark. Only A$80 here in Aus. Done! purchased..

[@TURALOWEN]

Thank you!

[@arnolddalby5552]

Loved neural nets since 1998 when I read a book which showed how 3 layer nets can solve difficult problems. In the 21st century the neural nets are magnificent and a credit to the brains of the human race. I am using a 21st century neural net myself and it's great. Hahahaha. Great video

[@MrFischvogel]

Thanks, Sir !

[@vinster9165]

KZbin read my mind this was exactly what I was curious about

[@dejavukun]

Thanks a lot to Steve and KZbin for recommending this great video

[@lucyoriginales]

Thank you... 💋

[@rohitschauhanitbhu]

Very informative

[@KelvinWKiger]

Ok, thank you.

[@juliocardenas4485]

Beautiful

[@mbonuchinedu2420]

Thank you Very much

[@9888622400]

Thanks!!!!

[@6lack5ushi]

Damn good video never knew I needed it but damn. Thanks

[@Eigensteve]

Thanks!

[@luiscordovadsgn]

Recommended gang, where you at?

[@Radictor44]

Me: Why am I watching a video on neural network architectures? KZbin: Start learning bitch

[@nex4618]

Thank you is all I can say but it doesn't feel like enough for this

[@mr1enrollment]

Steve: nice talk,... many questions come up, I'll ask a few 1)Do you distinguish planar vs non-planar networks? 2)Do RNN(s) become unstable? They look like control system time dependent processes. 3)Has anyone applied Monte Carlo toward selection of topology of a NN, or toward the activation function selection,...? Fascinating area to study.