This guy made all that really easy to follow. I admire his ability to explain such complicated things. He's really good at identifying and skipping over the irrelevant stuff, and focusing on the core problem/solution.
This is by far the best video I've watched on CNNs and I've watched 4 others. It really describes the back propagation and image compression to a single dimension.
convolutional neural networks is one of those things that really needs some visuals, i find that it is really hard to 'grok' when you get it explained in a book or via speech but once you get a visual example it's kind of hilariously simple and scaringly plausible
Neat, I just did my bachelor thesis on convolutional neural networks. We built and trained a sign language interpreter that worked pretty well. I can affirm that neural networks are equal parts wisdom and witchcraft.
+Alcesmire That's actually pretty awesome! It could seriously improve the lives of deaf people, especially seeing as how we're moving more and more into the whole voice-controlled, NLP virtual assistant world.
Frankly, I don't see the wisdom part. Sure, when you design a NN, you do have to scale it correctly to the problem and so on, but once you've got everything setup, the rest is magic.
Is'nt Nueral networks just math? Ive studied the backpropagation algo,stacked neural networks,etc..etc and the thing that struck me is that its all just math that you learn in an engineering course especially stats and linear algebra to solve equations. Why did you say its "witchcraft"?
Sreekar Nimbalkar Because NNs are large sets of linear or non-linear equations with dynamically generated coefficients that mean absolutely nothing to the designers of NNs, but somehow work. Hence witchcraft. I mean, I could basically simulate a NN on paper and it would still work, while I would still have no idea why it works.
Not long ago, I read about a machine learning system that was able to classify planes, trees, and people in nearly live video, all without ever having any hard-coded feature sets. The math was way over my head (despite being a computer scientist, specialized areas can still stump me at times). Now I look back at it, and it was in fact a CNN being used! This was a few years ago now, but if they just started becoming popular in 2012, that makes sense. Thank you for the higher-level explanation that allows me to understand it after all this time XD
My friend had some project with ANN. And I have some project with image analysis. I never knew both can be linked with this technique! This might help me in my research! Thanks Sean, thanks Dr Mike!
@@KnakuanaRka That changes meaning because a computer did it. Now you can automate that process and probably hundreds such processes. Automated object detection can be used in a multitude of processes and industries. Google it and surprise yourself.
So basically the whole convolution part is to "reduce" the dimensions, to then pass the information into a deep net? Really awesome videos ! Extremely addictive :)
In minute 9:07 - the size of the image changes not because it computes only the middle pixel, but because it fits in the size of the image less times than the images' width and height.
Wow exciting. Fantastic explanation and I can really see the power of this! Can't wait to see where deep learning and computer AI in general goes in the years to come. We are on the edge of some very exciting stuff.
Personally, I think hierarchical temporal memory will have a larger impact in the long run because it uses the brain as a constraint and won't have to rely so much on human ingenuity.
I love the topics from this guy and he explained it very well. Though his accent is a little difficult for me. Awesome video and very cool how you get to reference and link all those other previous videos
Thank you so much! Aside for entertaining me for years now, this video has actually helped me in my personal little research in programing an AI in a simple game using Tensorflow. (Is it overkill ? Sure. Is it fun to do and learn? Heck yeah!)
Another great video! I really like the fact that you create annotations for relevant or prerequisite videos and stuff, but maybe they would be more useful if they opened in a new tab. I don't want to lose where I was on this video, when I open and go through the annotated video.
I wrote a neural network framework in Java that allows you to build neural networks with arbitrary shapes and structures. You can chain layers together and as long as you implement forward and backward functions, your layer will work. I implemented a lot of layers (fully connected, convolutional, pooling, etc.) And yes it's Java but the process was a valuable learning experience, much better for me than learning keras or something without knowing how it works
Hey... I tried to implement a cnn from scratch using python... But it is not working properly... I found few issues but not the solution for it.. I searched abt it in every website but couldn't find proper solution.. can u clarify my doubts, pls?
Now I know what to work on between handing in my Master thesis and defending it. Cause model my Artificial Neural network towards a Convolutional Neural Network. Might indeed bring up my accuracies in regards to recognizing game events based on Electroencephalogram and Eye-tracking data.
+abschussrampe Google is currently pushing them onto basically everything. I'm not sure that _all_ their services use them yet but increasingly they do. For the following they either have talked before about _planning_ to use this technology or they already use it directly in the services you may or may not love to use by them: Google Maps Google Car driving Google Now suggestions Google Search UA-cam Thumbnails UA-cam video suggestions Google Translate Google Photos DeepMind (the guys behind AlphaGo) Allo, their new messaging app probably lots more Other big guns who either talk about or already do use these: Apple Microsoft Facebook Amazon probably lots more Nowadays, if you are on the internet, chances are you are using a service that in one form or another relies on deep learning and convolutional networks. You can do an insane number of semi-cognitive tasks with them. They _do_ have their limits in their current form but development goes rapidly.
+abschussrampe They're used in quite a few places. One example I saw used a CNN to classify the activities occurring in a video--for example, to learn how to tell whether a video is of someone hiking, mountain biking, swimming, or canoeing. It could be expanded with more data sets to classify other types of activity, which in turn would allow our future AIs to understand what's happening around them instead of having to be told what's going on before knowing how to react.
+abschussrampe Self-driving cars (Autos, you might say) are essentially all implemented as systems of CNNs at this point, if you want a particular example that will likely significantly change our way of life.
Rest assured the government is using them to identify not only who you are but what you're doing. We're going from facial recognition to activity recognition.
Absolutely fascinating! Great stuff, thanks for making it & sharing. I'm suspecting convolutional neural networks (CNNs) are possibly the solution for any potentially subjective classification, like the images in your video, but now wondering if a CNN is eventually equal to a Support Vector Machine for OBJECTIVE classifications, e.g. solutions to a mathematics problem.
Right, so you can adjust the weights with back propagation. I'm not sure if I understand how that works in detail, but I can imagine the principle. However, what concerns me is, how do you/how does the algorithm determine which combinations to make in all the intermediate layers? Mike was talking about how it might be edges, might be corners, might be brightness in the middle, we won't know, so we let the algorithm work it out, but how does it work this out? Because it sounds (from 9:55) like this whole thing has to be set up before we can test/train it to begin with.
knowing a little bit how the human visual system is working, it's seems like you're actually describing it... And that's scary and thrilling at the same time.
So really, Machine learning is creating an automated task to find enough differences that are unique to a specific thing so that you can then assume an outcome with enough confidence
I like how he throws around "corners and edges" and the begining of DL corners and edges was actually a prediction but in reality, the slices of the most capable nets looks absolutely nothing like corners and edges and a whole lot more like noise.
He said at the beginning that feeding a whole picture of pixels would be too much data and too many pixels but its hard for me to see why this method would be much better
So all the options you have for choosing what kind of new car you want to buy makes up a neural network. Right input and you get a possible car choice. How do you make it into a deep learning by-product. Ive seen curiosity used to train. This stuff is scary fun.
Presumably images is just one type of information you could feed into one of these. You could feed in text, or patient vitals/symptoms, economic data, etc..
+GoodWoIf Yep. Mike tends to focus on image analysis in his videos because he is, in fact, an image analyst, but CNNs can be used on pretty much any data set you can think of--as long as you have enough data to train them and a decent number of possible convolutions to apply.
The type of input matters because it helps when the input correlates strongly with reality. You could map every possible input to a pre-determined random representation, and it would probably have a lot more trouble, especially with generalization. It helps if similar inputs correspond to similar outputs.
How would you handle different sized images in the training data set? According to what I understood, the number of neurons (and weigths) depends on the number of pixels.
Interestingly, there is a platform out there called Accuval and they claim their house valuation is by far the best because they use ANN (fully connected rather than CNN).
You should use growing media-free hydroponic systems to for viewing healthy roots. I'm sure you are aware of the "speaking plant approach" or perhaps the benefit for using imaging and CNN's for monitoring crops, as plants don't talk binary...
+bibbly bobbly Thanks! Very interesting. I once read that the patterned hallucinations of LSD are probably caused by the acid disrupting the signal in our retina. Pretty interesting stuff.. Is that plausible in your opinion?
As far as I know, the kernels that CNNs learn on the first hidden layer (without prior knowledge) are also very similar to patterns our visual cortex reacts to. So it's maybe even closer than you thought it is. I definitely find this quite fascinating :)
The part of the brain responsible for most mammalian intelligence (the neocortex) is considered hierarchical by many, sort of like neural networks. But it's not like there's a single layer of neurons in each level. Each level has millions of microcolumns of neurons (around 100 neurons per column), 3 to 9 or more layers (depending on how you count and the location in the hierarchy) with distinct properties, and multiple types of connections (inhibitory, excitatory, modulatory, different durations of effect, etc.) There are hundreds or thousands of underlying common characteristics in the neocortex alone, whereas neural networks have maybe one or two dozen underlying characteristics. I suspect some of that is just plumbing to deal with things like metabolism, but neural networks (except hierarchical temporal memory, which doesn't do anything the brain definitely doesn't do) are pretty unlikely to lead to brain-like intelligence. They're really useful, but the way they are designed is like trying to reinvent the computer by mimicking transistors and no other characteristics of computers.
Classic neural net model doesn't work anyway. when you remove input from the node it still produces echoes of that input - that's how biological neurons work. How can you apply this to image processing? returning echo signals are learned via neural link storages for better results which system deems more favorable. Basically that means that your library can store node assigned info and each node instead of doing full processing theoretically can pull out saved neural link for just detecting a small portion of familiar input, run checks on that one and high-tune it to a degree when you'll need, say only 1/4th of sobel convolution etc.
4:00 - That is a somewhat misleading statement right there my friend! The reason why convolution is used is not due to the fact that it is needed to downsample the input space so that the computer will not melt; the reason is that if you dont use the kernel based method typically known as convolutions you lose ANY topological information that relates the pixels with each other. There is literally no chance that a multilayer perceptron type of neural network could achieve accuracy anywhere near a convolution neural network and the reason is as stated previously, that the topological relationship of the pixels is lost once its reshaped in a one dimenionsal vector with space equal the total amount of the pixels. In fact, latest architectures do not even attempt to downsample the information and pooling techiniques are considered to be obsolete, further proving how wrong the statement you made at 4:00 is.
I think for future videos you should set up the camera on a tripod and speak directly to the camera, to us. But otherwise this is very well explained and demonstared!
Very interesting stuff. Been working a lot with computer vision and at least tried a bit of ANN, so to see them combined like this is intriguing. I'm currently working with Fringe Projection Profilometry and I think that could be quite cool to show on this channel too. Basically it's a way to get a depth map of an object by projecting a sinusodial fringe image on it, and taking 3-4 photographs where the fringe function is in different phases. Then you combine these images with mathematics!
Is this the process that Google used for its image recognition software that can be run backwards to "dream up" images of the things it can recognize. So if the program can recognize an image of a cat you can reverse it and have it generate a picture of what it thinks a cat looks like.
+Jerrod Milton You are correct! How they basically do this is instead of adjusting the weights of the network to get the correct output value, they change the pixels of the input image, so that the CNN predicts e.g. a cat with higher certainty.
7:25 .. sir how the 60 filter outputs from the sobel operator has different outputs like corner detection,edge detection?Aren't the 60 output filters the same i.e, replica of each other?
+turarwanaa +J Simmons But doesnt it mean that its all just an optimization program that does the magic? The training images get run through the process, it produces a value. The settings are tweaked slightly, the result are compared and one is better than the other. Rince and repeat. Working with some optimization programs myself, the trick is in how the algorithm is programmed to make large or small tweaks to settings... Its like finding the tallest mountain in the area while blind... Does crossing the valley lead to a taller mountain or should you just go up hill? It all seems CPU horse power dependent to me o.o
Thomas Gandalf It isn't anywhere near brute force either. The "magic" is in why neural networks work so well at all compared to other methods. At a low level it looks fairly similar to other optimization methods, but the structure of the network and how it abstracts is very important. It makes much less sense than a naive perspective suggests.
+Vulcapyro something that runs to iteratively tweak parameters of mathematical formulae until it finds the best possible solution, i.e. explores or exhausts the state space, is pretty much the definition of brute force.... granted CNN don't usually exhaust the state space but make informed decisions on which parameter set to try next
Linux is better for long(multiday) computations because it is more stable and uses less resources in the background; it also happens to make the project more reproducible because versions of linux don't become dysfunctional with time like windows. Even if a CS has windows on their personal, there is no reason to think they wouldn't use linux on there workstation.
Don't say that we don't know if edges are important: the moment you add the convolution filters you state that. Convolution filters wouldn't work meaningfully for deep learning on noise.
+retepaskab No. The point of this technique is that you *don't* know what's important. You'd supply the network with as many different convolution filters as you can think of, not just what you think is important. The ones which aren't important will be down-weighted during learning, the ones that are will be up-weighted. But just because a convolution filter is included in the initial layer here doesn't imply it's important.
![The moment we stopped understanding AI [AlexNet]](/img/n.gif)
I like the 'extensive' library of books he has on that shelf above and behind him
Word Press Forms is all you need!
This guy made all that really easy to follow. I admire his ability to explain such complicated things. He's really good at identifying and skipping over the irrelevant stuff, and focusing on the core problem/solution.
This is by far the best video I've watched on CNNs and I've watched 4 others. It really describes the back propagation and image compression to a single dimension.
that was great as always! Now, eight years after, a video about Vision Transformers would be epic
i love watching mike out of all the other ppl on this channel. this man just sounds right
convolutional neural networks is one of those things that really needs some visuals, i find that it is really hard to 'grok' when you get it explained in a book or via speech but once you get a visual example it's kind of hilariously simple and scaringly plausible
damn, I thought I was the only one who read that book
Dog Barksley which book?
forshadowing
Neat, I just did my bachelor thesis on convolutional neural networks. We built and trained a sign language interpreter that worked pretty well. I can affirm that neural networks are equal parts wisdom and witchcraft.
+Alcesmire Aha! Now that I know neural networks exist, I can start my Mathematics thesis with:
"Let N be a neural network..."
+Alcesmire That's actually pretty awesome! It could seriously improve the lives of deaf people, especially seeing as how we're moving more and more into the whole voice-controlled, NLP virtual assistant world.
Frankly, I don't see the wisdom part. Sure, when you design a NN, you do have to scale it correctly to the problem and so on, but once you've got everything setup, the rest is magic.
Is'nt Nueral networks just math? Ive studied the backpropagation algo,stacked neural networks,etc..etc and the thing that struck me is that its all just math that you learn in an engineering course especially stats and linear algebra to solve equations. Why did you say its "witchcraft"?
Sreekar Nimbalkar
Because NNs are large sets of linear or non-linear equations with dynamically generated coefficients that mean absolutely nothing to the designers of NNs, but somehow work. Hence witchcraft.
I mean, I could basically simulate a NN on paper and it would still work, while I would still have no idea why it works.
For years down the line and look where we are now!
What a time to be alive!
2 minutes paper with Dr. Gibberish
Not long ago, I read about a machine learning system that was able to classify planes, trees, and people in nearly live video, all without ever having any hard-coded feature sets. The math was way over my head (despite being a computer scientist, specialized areas can still stump me at times). Now I look back at it, and it was in fact a CNN being used! This was a few years ago now, but if they just started becoming popular in 2012, that makes sense.
Thank you for the higher-level explanation that allows me to understand it after all this time XD
I could`t have explained it better, given the limitations of a youtube video. Well done computerphile!
I just discovered this channel, saw a bunch viideos and didn't come across a single boring one.
Watching this again really helped me improve my network. Thanks
Kudos to Mike, videos with him are always fun and well explained!
Thank you Computerphile for the great videos you put up.
I don't know why, but Dr Mike explains things so nice and clear. Thanks!
Couldn't but to observe WPF C# book , nice to see another one specialised in these two things
Might be the best intuitive description I've come across!
My friend had some project with ANN. And I have some project with image analysis. I never knew both can be linked with this technique! This might help me in my research!
Thanks Sean, thanks Dr Mike!
"...check whether the photo is of a bird."
"give me a research team and five years"
YES!!!!!!!!! GRANT MONEY!!! PAY ME! This is an ACADEMIC!!!!!!!!!!! $$$$$$$$$$$$$$
this is the best comment i've ever read. I feel you bro. Lived it!!!!!!!
SaltyBrains I don’t get how that changes the meaning at all.
Another xkcd fan?
@@KnakuanaRka That changes meaning because a computer did it. Now you can automate that process and probably hundreds such processes. Automated object detection can be used in a multitude of processes and industries. Google it and surprise yourself.
Still after 4 years, this is the best explanation of CNN on youtube ...
I guess the debate he mentions over whether neural networks will change everything, is settled now in 2024?
So basically the whole convolution part is to "reduce" the dimensions, to then pass the information into a deep net?
Really awesome videos ! Extremely addictive :)
In minute 9:07 - the size of the image changes not because it computes only the middle pixel, but because it fits in the size of the image less times than the images' width and height.
Wow exciting. Fantastic explanation and I can really see the power of this! Can't wait to see where deep learning and computer AI in general goes in the years to come. We are on the edge of some very exciting stuff.
Personally, I think hierarchical temporal memory will have a larger impact in the long run because it uses the brain as a constraint and won't have to rely so much on human ingenuity.
I love the topics from this guy and he explained it very well. Though his accent is a little difficult for me. Awesome video and very cool how you get to reference and link all those other previous videos
Me too, I enabled the subtitles, Any way its a great video
So happy for Frodo Baggins and his new career as AI teacher
This was super awesome, I love this Mike guy!
Thank you so much! Aside for entertaining me for years now, this video has actually helped me in my personal little research in programing an AI in a simple game using Tensorflow. (Is it overkill ? Sure. Is it fun to do and learn? Heck yeah!)
Idk how your game went, but you're the man.
Wow, interesting concept, nicely explained ... well done!
Another great video!
I really like the fact that you create annotations for relevant or prerequisite videos and stuff, but maybe they would be more useful if they opened in a new tab. I don't want to lose where I was on this video, when I open and go through the annotated video.
I love Mike's videos on image processing.. Keep em up!
A beautiful description! Well done.
update: they are indeed a big deal
amazing explanations here. Thanks for the share. This has made things more digestible.
Have to do a work on a paper about imagenet and deep convolutional neural networks. This video explained sooo much! Thank you!!!
Very cool video. I understood and saw clearly! Thank you so much for such content!!
I was trying to understand cnns and dr mike comes to the rescue
what a class! big shout out from Brazil
I wrote a neural network framework in Java that allows you to build neural networks with arbitrary shapes and structures. You can chain layers together and as long as you implement forward and backward functions, your layer will work. I implemented a lot of layers (fully connected, convolutional, pooling, etc.)
And yes it's Java but the process was a valuable learning experience, much better for me than learning keras or something without knowing how it works
The best way of learning is building stuff that you want to understand😉
@@IgorRoztr absolutely agree. i wouldn't say i understand something unless i've built something like it
Hey... I tried to implement a cnn from scratch using python... But it is not working properly... I found few issues but not the solution for it.. I searched abt it in every website but couldn't find proper solution.. can u clarify my doubts, pls?
I jst want to clarify whether whatever ik is right or wrong and want to know the solutions for the issues
Now I know what to work on between handing in my Master thesis and defending it. Cause model my Artificial Neural network towards a Convolutional Neural Network. Might indeed bring up my accuracies in regards to recognizing game events based on Electroencephalogram and Eye-tracking data.
Great and comprehensive video!
Extrordinary explanation, thanks!
hes best computerphile prof.. deep and on point.. would love to work with him :O
wow that was delightfully well explained, I enjoyed the video so much.
please ask him to talk about RNNs too!
3 deep 5 me learning
i dont understand what you mean
2 deep 4 them + inception
not 3 deep 5 me anymore tho
What an amazing explanation!
This guy is awesome
when you learning basic for a project and you find it's your supervisor in this video XD
I would have loved to hear examples of where these are getting used and what kind of impact they have on our way of life!
+abschussrampe Google is currently pushing them onto basically everything. I'm not sure that _all_ their services use them yet but increasingly they do.
For the following they either have talked before about _planning_ to use this technology or they already use it directly in the services you may or may not love to use by them:
Google Maps
Google Car driving
Google Now suggestions
Google Search
UA-cam Thumbnails
UA-cam video suggestions
Google Translate
Google Photos
DeepMind (the guys behind AlphaGo)
Allo, their new messaging app
probably lots more
Other big guns who either talk about or already do use these:
Apple
Microsoft
Facebook
Amazon
probably lots more
Nowadays, if you are on the internet, chances are you are using a service that in one form or another relies on deep learning and convolutional networks. You can do an insane number of semi-cognitive tasks with them. They _do_ have their limits in their current form but development goes rapidly.
+abschussrampe They're used in quite a few places. One example I saw used a CNN to classify the activities occurring in a video--for example, to learn how to tell whether a video is of someone hiking, mountain biking, swimming, or canoeing. It could be expanded with more data sets to classify other types of activity, which in turn would allow our future AIs to understand what's happening around them instead of having to be told what's going on before knowing how to react.
+abschussrampe Self-driving cars (Autos, you might say) are essentially all implemented as systems of CNNs at this point, if you want a particular example that will likely significantly change our way of life.
If you're outside the EU/Canada, Facebook uses CNN for facial recognition to tag photos
Rest assured the government is using them to identify not only who you are but what you're doing. We're going from facial recognition to activity recognition.
I am a neural network watching videos about neural networks.
we need to go deeper
this is amazing
Moeシt wow
aren't we all?
I am a neural network inputting and outputting comments about neural networks watching videos about neural networks. The singularity is nigh.
thank you for this video as well!
This guy seems cool - I like the videos he presents! :)
I like your thumbaround 0:30 :)
Great video... as usual.
The James Acaster vibes are so strong in this guy. Perhaps all this revising has turned my brain to mush, but this video really helped :D Thank you!
Absolutely fascinating! Great stuff, thanks for making it & sharing. I'm suspecting convolutional neural networks (CNNs) are possibly the solution for any potentially subjective classification, like the images in your video, but now wondering if a CNN is eventually equal to a Support Vector Machine for OBJECTIVE classifications, e.g. solutions to a mathematics problem.
Best nap ever
Right, so you can adjust the weights with back propagation. I'm not sure if I understand how that works in detail, but I can imagine the principle. However, what concerns me is, how do you/how does the algorithm determine which combinations to make in all the intermediate layers? Mike was talking about how it might be edges, might be corners, might be brightness in the middle, we won't know, so we let the algorithm work it out, but how does it work this out? Because it sounds (from 9:55) like this whole thing has to be set up before we can test/train it to begin with.
Matthew Taylor Right! I think I understand, and that makes sense. Thank you for the explanation.
knowing a little bit how the human visual system is working, it's seems like you're actually describing it...
And that's scary and thrilling at the same time.
Thank you for such great explanation !
So really, Machine learning is creating an automated task to find enough differences that are unique to a specific thing so that you can then assume an outcome with enough confidence
I like how he throws around "corners and edges" and the begining of DL corners and edges was actually a prediction but in reality, the slices of the most capable nets looks absolutely nothing like corners and edges and a whole lot more like noise.
He said at the beginning that feeding a whole picture of pixels would be too much data and too many pixels but its hard for me to see why this method would be much better
So all the options you have for choosing what kind of new car you want to buy makes up a neural network.
Right input and you get a possible car choice.
How do you make it into a deep learning by-product.
Ive seen curiosity used to train.
This stuff is scary fun.
The dog sound effect gave me a bit of a chuckle
Presumably images is just one type of information you could feed into one of these. You could feed in text, or patient vitals/symptoms, economic data, etc..
+GoodWoIf Yep. Mike tends to focus on image analysis in his videos because he is, in fact, an image analyst, but CNNs can be used on pretty much any data set you can think of--as long as you have enough data to train them and a decent number of possible convolutions to apply.
Very well explained!
Man, i would really like to see the work behind the root tips convolutional network.
My intuitiion says that using an HSI image format would have much better results than rgb as shadows would be simplified.
RGB was just the example used here, I don't think he literally meant they use RGB to produce different versions of the image.
Heck, three of the convolutions might well convert from RGB to H, to S, to I, and/or some combination of them.
right, the information is exactly the same so it might not matter to the Net
The type of input matters because it helps when the input correlates strongly with reality. You could map every possible input to a pre-determined random representation, and it would probably have a lot more trouble, especially with generalization. It helps if similar inputs correspond to similar outputs.
Great video! I would love to see more A.I. related stuff.
We want video about PNN(Probabilistic Neural Network)
Dang kernel convolutions.
My least favorite thing to happen when I'm making popcorn.
Expected a video about neural networks analyzing sentiment to help news outlets adjust their narrative :o
How would you handle different sized images in the training data set? According to what I understood, the number of neurons (and weigths) depends on the number of pixels.
Interestingly, there is a platform out there called Accuval and they claim their house valuation is by far the best because they use ANN (fully connected rather than CNN).
You should use growing media-free hydroponic systems to for viewing healthy roots. I'm sure you are aware of the "speaking plant approach" or perhaps the benefit for using imaging and CNN's for monitoring crops, as plants don't talk binary...
So it turns out these are in fact a big deal.
Wow, "convolution process" just sounds a lot like abstraction that brains do....I think these guys are really onto something here...I dig it
+Christopher Willis really interesting way of looking at it.. what an exciting time to be alive!
+bibbly bobbly Thanks! Very interesting. I once read that the patterned hallucinations of LSD are probably caused by the acid disrupting the signal in our retina. Pretty interesting stuff.. Is that plausible in your opinion?
+bibbly bobbly thanks well said
As far as I know, the kernels that CNNs learn on the first hidden layer (without prior knowledge) are also very similar to patterns our visual cortex reacts to. So it's maybe even closer than you thought it is.
I definitely find this quite fascinating :)
The part of the brain responsible for most mammalian intelligence (the neocortex) is considered hierarchical by many, sort of like neural networks. But it's not like there's a single layer of neurons in each level. Each level has millions of microcolumns of neurons (around 100 neurons per column), 3 to 9 or more layers (depending on how you count and the location in the hierarchy) with distinct properties, and multiple types of connections (inhibitory, excitatory, modulatory, different durations of effect, etc.) There are hundreds or thousands of underlying common characteristics in the neocortex alone, whereas neural networks have maybe one or two dozen underlying characteristics.
I suspect some of that is just plumbing to deal with things like metabolism, but neural networks (except hierarchical temporal memory, which doesn't do anything the brain definitely doesn't do) are pretty unlikely to lead to brain-like intelligence. They're really useful, but the way they are designed is like trying to reinvent the computer by mimicking transistors and no other characteristics of computers.
good explanation
Classic neural net model doesn't work anyway. when you remove input from the node it still produces echoes of that input - that's how biological neurons work.
How can you apply this to image processing? returning echo signals are learned via neural link storages for better results which system deems more favorable. Basically that means that your library can store node assigned info and each node instead of doing full processing theoretically can pull out saved neural link for just detecting a small portion of familiar input, run checks on that one and high-tune it to a degree when you'll need, say only 1/4th of sobel convolution etc.
I LOVE these technical video's. It would be great to see such video's on hardware too.
4:00 - That is a somewhat misleading statement right there my friend! The reason why convolution is used is not due to the fact that it is needed to downsample the input space so that the computer will not melt; the reason is that if you dont use the kernel based method typically known as convolutions you lose ANY topological information that relates the pixels with each other. There is literally no chance that a multilayer perceptron type of neural network could achieve accuracy anywhere near a convolution neural network and the reason is as stated previously, that the topological relationship of the pixels is lost once its reshaped in a one dimenionsal vector with space equal the total amount of the pixels. In fact, latest architectures do not even attempt to downsample the information and pooling techiniques are considered to be obsolete, further proving how wrong the statement you made at 4:00 is.
Would you mind putting links in the description for annotated link videos for us mobile users, thanks.
+Sirus done >Sean
Thanks
Thanks. Annotated links are pretty much always off in the future. At least for nerds?
eyyyy mate, i-m from your uni!! birmigam is so cooool!!!
Would be nice to see you talk about RNNs
Thanks for the wealth of information!
I think for future videos you should set up the camera on a tripod and speak directly to the camera, to us. But otherwise this is very well explained and demonstared!
Great video thanks.
Very interesting stuff. Been working a lot with computer vision and at least tried a bit of ANN, so to see them combined like this is intriguing.
I'm currently working with Fringe Projection Profilometry and I think that could be quite cool to show on this channel too.
Basically it's a way to get a depth map of an object by projecting a sinusodial fringe image on it, and taking 3-4 photographs where the fringe function is in different phases. Then you combine these images with mathematics!
great video
Are there different ways to implement the library? How do people in competitions make their algorithm better using the same library?
Is this the process that Google used for its image recognition software that can be run backwards to "dream up" images of the things it can recognize. So if the program can recognize an image of a cat you can reverse it and have it generate a picture of what it thinks a cat looks like.
+Jerrod Milton You are correct! How they basically do this is instead of adjusting the weights of the network to get the correct output value, they change the pixels of the input image, so that the CNN predicts e.g. a cat with higher certainty.
+Karl Kastor Wait, this exists? Are there front-ends to those applications available?
Benjamin Philipp Google Deep Dream. People have done several implementations for this since the original paper.
Benjamin Philipp
google Deep Dream. People have done several implementations for this since the original paper
Karl Kastor
Tanks - I've since found Deep Dream, but thanks for coming back for me :)
7:25 .. sir how the 60 filter outputs from the sobel operator has different outputs like corner detection,edge detection?Aren't the 60 output filters the same i.e, replica of each other?
Dr Mike Pound can you do a video about how karnel works, please?
Extremely good explanation of things that, until this series on deep learning, were just black magic to me !
+turarwanaa Lucky you, watched it twice and I still think he is a dark wizard.
+turarwanaa +J Simmons
But doesnt it mean that its all just an optimization program that does the magic?
The training images get run through the process, it produces a value. The settings are tweaked slightly, the result are compared and one is better than the other. Rince and repeat.
Working with some optimization programs myself, the trick is in how the algorithm is programmed to make large or small tweaks to settings...
Its like finding the tallest mountain in the area while blind... Does crossing the valley lead to a taller mountain or should you just go up hill?
It all seems CPU horse power dependent to me o.o
+HexerPsy yes it's pretty much brute force, no magic. as with most things machine...
Thomas Gandalf It isn't anywhere near brute force either.
The "magic" is in why neural networks work so well at all compared to other methods. At a low level it looks fairly similar to other optimization methods, but the structure of the network and how it abstracts is very important. It makes much less sense than a naive perspective suggests.
+Vulcapyro something that runs to iteratively tweak parameters of mathematical formulae until it finds the best possible solution, i.e. explores or exhausts the state space, is pretty much the definition of brute force.... granted CNN don't usually exhaust the state space but make informed decisions on which parameter set to try next
That "Pro WPF for C# 2010 book at the back". :)
"I'd have to start by programming up linux" he says while sitting in front of a WPF book
...because Computer Scientists are only allowed to reference one Operating System? I don't get it.
Linux is better for long(multiday) computations because it is more stable and uses less resources in the background; it also happens to make the project more reproducible because versions of linux don't become dysfunctional with time like windows. Even if a CS has windows on their personal, there is no reason to think they wouldn't use linux on there workstation.
agreed he is not capable of this but he doesn't care because he has WORK TO DO.
That is AMAZING!
Don't say that we don't know if edges are important: the moment you add the convolution filters you state that. Convolution filters wouldn't work meaningfully for deep learning on noise.
+retepaskab No. The point of this technique is that you *don't* know what's important. You'd supply the network with as many different convolution filters as you can think of, not just what you think is important. The ones which aren't important will be down-weighted during learning, the ones that are will be up-weighted. But just because a convolution filter is included in the initial layer here doesn't imply it's important.
+retepaskab Also, what would you possibly learn from noise?
MAny thanks for LECTURE