How Deep Neural Networks Work - Full Course for Beginners

I just woke up. I am very confused. Why am i here-

Just woke up, don’t know where I am or how I ended up here

Let me guess: you just woke up and this video was playing

So, I just woke up to this video on my phone but the ironic part is I just learned about this yesterday.

dunno why but this video was playing when i woke up in the middle of the night

i slept watching a different completely unrelated video and woke up on this what just happened

Watching this on my way to sleep for all the people who are waking up to this, it might break the cycle. 🙏💪

Somehow this autoplayed on my phone while I was sleeping.

I came with an interest in neutral networks.
I left feeling well rested.

I fell asleep watching a very simple maths video and woke up to this after dreaming that me and my friends were studying its contents. I’ve never done anything to do with this before but I understood it when I was dreaming about it so will probably give it another listen. It reminds me of being in College/University when SWIM was doing a bunch of drugs and accidentally designed a computer brain. Score for drugs 1,264,273,995,267,177, score for sobriety: still zero LOL

From sleeping on a Geopolitics video to landing here, I am stunned😅

So to be the first, I’d just like to say my journey consisted of falling asleep to a video about why a magnet on the front of a car wouldn’t work, then it went to cursed units of measurement, then it went to professor Dave explains and then I ended up here, all in all I’ve been asleep for about 3 hours and I need more sleep…
Anyone else wanna share the journey?

I woke up and this was playing on the background

Just woke to this playing. It was the catalyst to the craziest most vivid dream since childhood...im in my 30's.

KRplus is a good detector of sleep

Very surprised to see everyone woke up to this video as well. The algorithm strikes again!

Apparently everyone waking up to this including myself 😂

what is this, i just woke up..

I watched this on purpose. :) Found it quite helpful! Cheers

Assuming that everyone has had or currently has a learning capacity you realize that environment plays a huge part.

I’m pretty happy to be awaken by a such interesting lecture. Will watch it again

If you're an audio guy, Squash functions are just compression by factor of Ratio (r). Threshold is the pickup weight input and knee is smoothing of weights between input and output over a certain range. And there you go. Compression in a nutshell. However the dB peak scale is non-linear. The dB scale is power of 2x10dB. That's what makes it the most confusing.
So a ratio of 10 to keep it simpler is double the volume at the threshold gradually weighting less until the set peak where compression is zero. The knee rolls off that effect by a dB factor at a specified loudness and breadth of its impact. Seems gaussian to me. I don't know how the math works at the knee but it gives a smoother transition from boosted to left alone. So in a typical simple compression threshold at -24dB with 10 ratio would result in threshold at -12dB tapering to -10dB, -8dB, -6 and so on until you hit zero assuming your highest peaks are 0dB which is bad. Then you adjust the output to -8 or -14 depending on the sound and that scales the whole curve downward unaltered relatively by whatever output dB you set. If your threshold was boosted by compression to -12dB and you scale it down in output by -8dB then your threshold after processing will be -20dB tapering off up to -8dB in the same curve it had before the output was scaled down.
That's why you have to adjust input vs threshold vs ratio vs knee vs output to get the best out of simple compression. Multiband compression is the same thing just much more complicated as it accounts for frequency where you can specify within a certain frequency range how much compression you'd like. Overlap them and yeah that gets quite complicated but it's super useful to getting the right sound especially in dialogue to grab and manipulate the loudness of tonality and sibilance while rejecting the background noise or any echo or unwanted reverb.
The same principles apply in NNs in more of a deterministic and mathematical way. It entirely depends on the architecture and what it is used for as you are taking a larger dynamic range of inputs and compressing them to a smaller range of outputs. That's why CDs in the 90's Redbook audio was 16 bits wide. 2^16 made for 65536 levels of volume for any given sample. That was enough because it was replacing cassette tape which had horrible dynamic range. Now it's standard to have 24 bit audio which has a vastly higher dynamic range of 16,777,216 levels of volume at any given sample. For production and processing it's common to have 96 bit audio which has 7.92281625 x 10^28 levels of loudness. That's technically not better than analog but no human would ever be able to tell the difference. It helps computers and audio processing make very very accurate changes.

I suddenly opened my eyes and dreamed about this video while sleeping with my tai chi instructor at my beachfront property. Unreal.

Excellent explanation, excellent figures, and animations, awesome speaking! Looks like a dream course!

Wow first time I’m actually glad I learned calculus in school. Nice to see it useful outside of the classroom.

Thanks 🙏 I finally have some understanding of why cnn’s work!

Thank you for making this!

I knew an engineer brother of a friend who was working on how best to implement gradient descent into NNs years and years ago. I think he was one of the ones who gave up before CNNs became a widely used method. He certainly isn't a NN engineer anymore. He went on to predictive logistics which resembles RNN but really it was a much simpler feedback loop and balancing input versus output. Part of the Just in Time production to delivery process. Likely, the processing power and tech in the 90's wasn't powerful enough to realize the emergence big data is capable of now. Kinda wonder what he would have done had he been doing that 25 years later than he was. I know that he uses advanced NNs now of various types for his job but at this point he is an implementer rather than a developer. Tuning plays a big role.

Everyone just waking up in this video

I just woke up and this was playing. Now I better just know how to program a new LLM or I’ll be really upset.

This is super fascinating

Wonderful, elegant explanations! This is the way to present the basics of a hugely scalable system!

What should be confusing is your vision or at least your conclusion! but the global context is great

KRplus really just teach me Neutral Networks while I’m asleep.

Just woke up after it ended. I remember waking up for a few seconds thinking that it was interesting, hitting repeat and fell asleep after a few seconds again as it was still in the middle of the night.
Now I'm wide awake and hit replay again to truely watch it haha 😂

Why have so few actually chosen to watch this video, I woke up at 4am to it playing

This is such a great tutorial! Thank you for making it. I will share the video with students interested in neural net and deep neural networks.

This is a lot of videos smashed into one. Honestly, excellent work.

That's strange, I just woke up exactly 7:30 am to this video playing. And I went to the comment section and turns out I wasn't the only one. I don't remember watching science related video neither.

If you can us any shape coulad you use a Golden Torus mandala or hipnotic eye. I would try a this shape but it would Spiraled Like a slinky within a slinky?

Marvelous work! If this captivates you, there's a book with similar themes you’ll want to explore. "From Bytes to Consciousness: A Comprehensive Guide to Artificial Intelligence" by Stuart Mills

Nicely explained. One thing near the start is that sigmoid only goes from 0 to 1 (It's tanh (x) that goes -1 to +1)

Very clear.....thank you

One of those videos you get hooked to when backed

Simple and intuitive explanations. Thanks!

2:27:45 So your neural network has a neural network lol... I was thinking along similar lines right before you said this... "It could translate to the "rough doesn't fool a human" and then be "translated" again by a network that understands the specific language quirks better and has been trained on natural speech... But the straight intermediate (Latin? Lol) sounds better... I don't guess it even has to be a real existing language even, so long as it is set up to minimize "lost in translation" errors.
You've got one of the clearer presentation methods for me... This stuff is really making sense now finally lol

Thank you for the video :)

This is great.learning neural networks while I drive to work. The internet is beautiful 😍.

Brandon; great video! where can we find more visual representations of adding curves? @40:00 you begin to combine curves. how and where does one learn more?

Woke up to this and is exactly what i searched for yesterday, but couldn't find it

Wishing you goodluck keep it up 🙏

Thanks!

I woke up to this like everyone else, apparently.
I'm guessing the unusually long runtime increases the likelihood that someone would, as opposed to waking up on some random 20-minute video 🤷🏻

I am also here on complete purpose. And can’t stop watching.

Fell asleep to a Mr Beast video, greeted by a full in-depth understanding of how neural networks work when I woke up

Fantastic explanation 👌

I just woke up and turned my phone on to this??

much respect

Just woke up. Saw this in my recommended section and immediately clicked, because I didn't understand the datascience introductory course I had.

Please make a video on computer networking and competitive programming

so glad im not the only one who woke up to this vid in the middle of the night lol

Woke up 2 hours 44 minutes in , i am CONFUSHON

7:40. The bottom right neuron is supposed to be inverted. 2 black on top and 2 white on the bottom. The negative weights should actually be positive weights.

i clicked on this because i was curious just to discover that i watched the entire thing sometime when i was sleeping

I was not watching this when I feel asleep.... but I did wake up to it

Thanks for sharing

Watching this before the TensorFlow tutorial.....

46:28 in b those have a corelation too

BEST CHANNEL, THANKS FOR FREE STUFF.

So if they are saying the jet stream will straighten that intern will reduce the strength of low pressure systems.
Also if the jet stream drops lower that will then make more areas cooler.
P.s the golf stream is not the same as the conveyor belt

3:33 How come the narrater's cough makes the result go from negative 0.075 to positive 0.075?

Guys how about a Tensorflow tutorial in depth! Please?!!

i was thinking who in the world would watch something like this 3.5 million times

So super quick, the brain is measuring space time and aligning to it and the more it has to cross reference and integrate, the more data to pull on and weigh the differences, the more aligned the brain is to the current. So the images and thoughts are measurements so not entirely false. If it’s a measurement then we align more to be more accurate. We aren’t really finding “unreal” that’s just a word that means it isn’t current not not existent. Like a chair in a tree or a child before it’s born. Potentials waiting to be exposed by measurements and then reflected through the portions we can bring it out. I know it sounds crazy but the images you see in the head aren’t real but can be pushed that direction through the measurements and reflecting them through our actions and such.

Fantastic, thank you.

1:20:40 Since this course is about learning algorithms this is important to classification. Vector:
noun
Mathematics.
1: a quantity possessing both magnitude and direction, represented by an arrow the direction of which indicates the direction of the quantity and the length of which is proportional to the magnitude.
2: such a quantity with the additional requirement that such quantities obey the parallelogram law of addition.
3: such a quantity with the additional requirement that such quantities are to transform in a particular way under changes of the coordinate system.
Biology.
1: an insect or other organism that transmits a pathogenic fungus, virus, bacterium, etc.
2: any agent that acts as a carrier or transporter, as a virus or plasmid that conveys a genetically engineered DNA segment into a host cell.
Computers.
1: an array of data ordered such that individual items can be located with a single index or subscript.
verb (used with object)
Aeronautics.
1: to guide (an aircraft) in flight by issuing appropriate headings.
Aerospace.
1: to change the direction of (the thrust of a jet or rocket engine) in order to steer the craft.
...
I am tempted to say Physics would include a unit
Physics
1: a quantity possessing magnitude, direction and unit.

If only life was like that one episode of Dexter. I’d be a quantum physicist by now

Seems like neural net teaching comes in two forms - math or python libraries. It'd be cool to see an intermediate form - some code that implements the math but isn't a library.

I just woke up.. went down watching vertasium

My auto play was turned off when I went to sleep and yet, somehow, I woke up to this playing. Not sure if I turned it on in my sleep or something but then again it seems I’m not alone in the endeavor

I have a project where I'm taking some low resolution monochrome sprites and I'm attempting to reduce the shape of these sprites in a higher resolution. I tried to play with some scale filters to aid me in that process. It seems a lot like trying to make a vector font from an 8x8 character bitmap.

Amazing video, thanks!

41:48 imagine it in n-dimension
It can do amazing stuff in a matter of second.
Can you explain neural net in time series please
These lessons touch my heart and for the first time i can imagine abit what is going on in that black box

I woke up and this was on my phone lol

@3.56, the activation function shown is tanh (-1 to 1) not a sigmoid/logistic (0 to 1).

I'm only a minute or two in but this is amazing... I'm understanding it so well that I'm considering learning to code

For busy students: play at 1.25 or 1.5 speed

Woke up to watch 😮

3 am be hitting when thes videos show up on my feed

This was on with no way to stop it for several minutes. I think Murphy was trolling me today

I fell asleep with my PC on When I woke up I saw this. Interesting. I did take differential calculus in college and programming for other reasons. Now I have an understanding of neural networks.

First 30s wrong example but good video in general. Yes, you can simply detect what pixels are dark, what are lighter, there is function for that. Also 4 pixels is bad example because you could create like 8 if statements define tolerance (same tolerance as in Photoshop which is distance between colors) and it would outperform every model.

I, too, just woke up to this video - after a nap while on holiday. Lol

This already looks like an interesting course , thanks FCC !!

1 am and he finally hits me with the human level intelligence section

@7:40 at the last bottom-right neuron on the third layer; shouldn’t the connected weights be positive (white) to get the desired output of the horizontal pixels?

I woke up with the video already finished darn it

A simple rule definitely could solve the first problem. I'm not sure that's a very compelling argument to use a neural network in that case.

For not busy students, watch at 0.75 or 0.5x

So in slept with the video 1 trillion lions vs the sun and now I woke up 1:38:12 into this video