“Digital signal processing is a veritable ocean. Take as many soundings in it as you will, you will never know its depth.” [Paraphrased from “Le Pére Goriot” by Honoré de Balzac, 1835] (i copied this one from dsprelated forum, a message of Rick Lyons

Amazing 🎉🎉🎉

Fascinating explanation.

this is good. its a video that needs to be appreciated.

I think I found a mistake in the paper. In section 2.1, shouldn't the "set of possible new states and corresponding responses" be a subset of the power set of Q × R not Q × R itself, making the definition of Δ read "∆ ⊆ Q × Π × O × P(Q × R)"?

An algorithm that solves any of the NP-complete problems in polynomial time would be a de facto proof that P=NP. Proving P /= NP requires showing that no such algorithm can exist. For P /= NP there is no shortcut.

Great video!

Amazing ! Where is the source code ?

I guess this man has never heard of the Sloot Digital Coding System. 🤣

Sparsity, dimensionality reduction and Interprtability

please do more video's bud

Avalanche will be a huge network in the future [date of this comment 15.03.2023]

الحمد لله الذي بنعمته تتم الصالحات، مسلم يشتغل في الخوارزميات و نظم المعلومات المكان الطبيعي لأمة محمد صل الله عليه وسلم.

Миха, пожалуйста, ещё раз и на русском!!!)))

Amazingly boring, dry. I keep lookin gfor a more learner-friendly FFT video.

1. Supervised learning want unsupervised. 2. Why do machine learning models work? New mathematics for machine learning. 3. Network adaptability. 4. Scaling a system.

Can you write out the 4 challenges.

I'm a bit confused. When I place - say - 10 microphones into a room and let them record an audio signal, I don't get a 10-dimensional signal. I get 10 separate 1-dimensional signals, which is a very different thing - as different as an image is from a stereo audio signal - the image is truly 2D whereas stereo audio is 2-channel 1D. Number of channels and signal dimensionality seem to be conflated here, unless I'm missing something. Likewise, 10 cameras in a room would capture 10 separate 2D signals, i.e. 10 channels of a 2D signal (which could themselves actually be 3-channel data for RGB). ...or maybe 3D, if they capture video and time is taken as 3rd coordinate

Signal processing is the future!

Crash! 😋

Subtitles please please

OCaml came out before Scala and combined OO and functional. Although not everybody considers it a "major" language. F# came out around the same time.

Amazing professor with amazing teaching skills! I strongly recommend his DSP specialization on Coursera which gave me a fresh perspective on signal processing.

I think he audio is not synced with the video..

nice job misha!

Guys, this was truly amazing. Nicely done

macam mana nak buat?

「上記のギフトのいずれかを選択できます」、

Brother I know mechanical engineers could find resonance but when I had a deep thought on this resonance Is an slow accumulation of energy which is accumulated very high in small installments when the frequencies match if you strike a turning fork of 50 hz you get the same frequency of vibration on another tuning fork so they both vibrate if you strike it harder the amplitude changes hence loudness is a human factor the frequency is the same the languages that human speak through out the world the sound only resonate your ear drum for few seconds my question is that the harmonics is the fundamental frequency and overtones are the frequency that follow it take a word in any language you spell it according to convolution the thing scales and ques and stack the signal so convolution can be used to model resonance so when your ear drum vibrates it vibrates so the electrical signals are carried to brain like tuning fork ear drums vibrate within the audible spectrum 20 hz to 20000 hz hence resonance is caused by the word we speak and within the audible range the ear drums vibrate and we make sense of words I have seen in one videos on KZbin that due to harmonics in any sound causes resonance which could be modelled by convolution recalling the resonance its destructive because slow and steady accumulation of sound on the mass causes high stress and high energy to build inside and stress increase and the system fractures or collapses but our ear drum hearing the sound from human languages try to vibrate but why our ear drum when subjected to continuous exposure of sound does not fracture or rupture like a wine glass iam not telling about high loud sound higher than 80 db but a audible range sound within the frequency of 20 hz to 20000 hz under continuous exposure why it's not damaging it again not failure by high energy but low one in synchronisation on air . But I tried it in my students when I told them to be quite in class they did not listen to me so I took my phone and set an frequency 14000 hz and they told it was irritating the idea of resonance is "small effort but large destruction " just like Tacoma bridge where the wind just slowly accumulated energy on the bridge and it collapsed it so my conclusion is if an audible frequency at continuous exposure to an human ear can it cause bleeding again "small effort but great destruction" sorry for the long story I you are able to reach hear you must be as curious as me so still not finished the ear drum is shook by harmonics in the sound we make by the words( or )overtones in the sound we make by the words I know harmonics is the fundamental frequency and overtones are following it which under slow and steady accumulation of sound energy resonates and could damge the ear drums again "small effort but big destruction" not to mention we assume the person is in coma or brain dead hence when the sound irritates him he or she could not make a move so my question is so simple normally human ear responds to harmonics or overtones according to convolution which could be a disaster but with minimal effort 🙏🙏🙏🙏 at here I could be wrong because harmonics can also be used to construct sound so can it be destructive or the overtones which are the trouble makers and which one according to this gives a response curve when two signals convolved by harmonics or overtones which is destructive but with minimal effort and convolution happens when ear drums oscillate is by harmonics or the overtones or also the trouble makers there And also in harmonics the sound is constructed when the frequencies making them are played simultaneously it can construct any sound hence it's called the signature of sound hence when ear drum responds to these harmonics akA specific harmonics the Fourier transform is playing these signal simultaneously by decaying the signals at the end hence by signal emmision will construct any sound audible or not audible hence the frequencies are in harmony and orchestrated by Fourier trans form algorithm in ou brains I guess Correct me if iam wrong Telling the application of convolution in terms of signal processing may help a part of people in the telecommunication and eee engineers but when convolution meets signal processing it also is used as a filter but my question is I have read convolution even it's useful for mechanical engineers such that in resonance when a failure occurs not due to massive force hitting an object results in large deformation could cause failure or an large impulsive force acting on it for a duration of time could cause failure but there is an another phenomenon where the natural frequency of any object is reached the energy builds in it very high and could cause a failure in this manner a small disturbance which accumulates over a time and causes a high energy to build in the system due to energy very high it causes stress and the system collapses this is highly different from stability perspective of control system being not stable does not mean it's accumulating energy inside it but in case of amplifier there is an capacitor or inductance device which causes the attenuation in the electrical signal and filters some frequencies but in other perspective amplifier amplifies the signal such that it stack ques and scales the signal but I don't know this is done by capacitor or am inductor but convolution is useful to both mechanical civil eee ece and every applied scientist and engineers hence it's used as a filter in an circuit or used to amplify but even transistor amplifies the signal without an capacitor or an inductor I guess also mechanical engineers can use it to model resonance hence the energy inside the system build high by periodic accumulation of the system reaching its natural frequency which leads to failure and I can also tell you that when amplifier filter or amplifies the signal it used convolution hence it's useful to every applied scientist and engineers but not to mention the pure Mathematicians use it of convolution of kernels thankyou guys some of my inference could be wrong if somebody or the author of the video is familiar with it please correct the above and educate me thank you for the wonderful video sir Hence the professor told we could avoid some coefficients as we avoid some overtones and noise in harmonics the sound can also be constructed by simultaneously playing a simple beep but instead of doing it manually this is done analytical by Fourier transform

a truly exemplar explanation of entropy , "The Number of Binary Questions you need to ask, so 1 bit corresponds to 1 binary question"

Specify the goal and need money

Excellent. I wish signal processing community will explore the physics of deep learning.

♫ One love, ONE-HOT Let's get together and feel all right Hear the children cryin' (one love) Hear the children cryin' (ONE-HOT) ♫

I think you should add “a philosophical point if view” to the title

Damn, I'm impressed <3

It sounds like an interesting problem. Unfortunately, I can't find much information on the net. So your video is a very valuable.

Wow

in b4 this blows up.

The video sound is pretty good, beyond my imagination

AND IM THE SECOND

The video sound is pretty good, beyond my imagination

The video sound is pretty good, beyond my imagination

wow!! He's the founder of avalanche

Amazing lectures! If possible would you make the distributed computing and cryptography classes in EPFL public?

Excellent video, thank you for this.

Is there any playlist of these lectures?

Not sure the video did a very good job explaining exactly what proof of bandwidth is. Perhaps it is just my mental capacity that is lacking here, but I still don't understand how pobw is a replicable consensus to pow. That said I am still very curious to learn more.

Nice work, have a look at what I did in deconvolution in 2000.

love this video !

Absolutely.Martin developed a theory and is now proven to be right. Very nice video.Thanks