Autoencoders Explained Easily
Вставка
- Опубліковано 28 тра 2024
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Learn how autoencoders work in an intuitive way. Learn about representation learning, latent space, and other fundamental concepts. I also explain how autoencoders are applied to important tasks such as data generation and denoising.
Slides:
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Context
0:00 Intro
1:17 Key idea in autoencoders
3:03 Encoder
4:15 PCA vs Encoders
7:05 Decoder
8:21 Training autoencoders
10:13 Optimal autoencoder
12:57 Deep Autoencoder
14:11 Deep Convolutional Autoencoder
16:15 What's the point of compression/decompression?
17:12 Autoencoder applications
17:51 Generation with autoencoders
22:29 Denoising with autoencoders
26:06 Anomaly detection with autoencoders
26:39 Coming next - Наука та технологія
Eventually, after 6 useless videos on this topic, I found this one that clearly explains why, what and how autoencoders are doing! Great video! Bravo Valerio!
Thanks!
I am reminded of my boss asking for a 10 page report, and then asking for a 1 page summary. After some deliberation, he would again ask for expanding the summary to a 3 or 4 page report. What would happen is the important elements would get picked when we reduce and these would be elaborated when we expand again.
Intuitively one catch would be the error should not be minimized to a very low value. Perhaps we would call that over fitting. The balance may be in how high is the correlation retention relative to data reduction.
Valerio thanks so much for putting together such great content and providing insights into Machine Learning concepts!
This channel is gold!. It really helps to understand Ai, and its "convoluted" topics. The terminology really confuses me, but you manage to explain it in a very simple way. Watched 3 videos already ... more to go!
Well done. Great explanations without being oversimplified
Thanks for the great presentation that does not make the subject so complex and keep it as simple as possible.
Epic. Just what i was looking for
27:04 yay, looking forward to watching the next video. Thanks for making this series.
Love all your videos! Looking forward to the next video from this series :)
Thanks!
very well explained, in perfect detail. thanks a lot man
Don't have any background in CS, but,2 mins into the video and you are so easy to understand.
Great video! When are you going to publishing a video for anomaly detection?
Great video Valerio, thank you so much!
I don't know why, but it was so easy to understand it once you made the 2D plane instend of two nodes there. Awesome
Excellent! Can't wait for next video!
I really enjoyed every secound, it was very helpful. Thank you :)
Thank you for this video. Very easy to understand ❤
Oh my!! Your video made a lot of sense!! thanks a lot and subscribed!!
cool! just gave me a research idea x) nice, clear and simple!
Glad I could help :)
Awesome!! Best explanation I have ever seen 👏👏🙌😍😍
Extremely useful and easy to understand 🎉🎉🎉❤❤❤thank you so ooo much
Great job! Thanks for sharing😊
Great video! I am keen to see the implementation of this.
What are your thoughts on using GANs to generate spectrograms?
GANs are also effective at spectrogram generation. I'll definitely cover them at some point in the future.
@@ValerioVelardoTheSoundofAI Please conver them. I am waiting for that for over a month now and there is no other channel which cover audio processing as you 😃
great work sir
my question is can we use kernel pca as encoder in Autoencoders because as I know it can reduce the dimensions of nonlinear data
Thanks a lot Valerio, I really like the way you explain all of these "non-obvious" stuff !
I would really appreciate if you could make a video which deal with AEs for Anomaly detection.
Do you still plan to cover this item, as you mentionned in this video ?
If so, by when do you think possible to have it ready ?
Thanks again, and above all : keep on going !
Thank you Gildas! I intend to cover AEs for Anomaly Detection in a self-contained video / project. Not sure when it'll come out, but it's in the pipeline ;)
@@ValerioVelardoTheSoundofAIThanks a lot Valerio! I am new to the field of audio signal processing and especially to AI approaches. Your channel helped me a lot. It was perfect to get an understanding of the whole topic. I am currently writing my master thesis in the field of anomaly detection of engine noise. I wanted to use an autoencoder as an example for the detection. I would therefore also be interested to know whether you are planning a video on this or whether it is already available on your channel. Thank you for the work and time you put into your channel! Keep up the good work! :)
Great explanations! One question though. In the denoising example, how different is using Autoencoders from regular deep learning? I mean, you feed in the audio with the noise and test your results against the clean audio which kind of acts as the label to the data.
please make a video on semi-supervised learning along with practical implementation.
Thanks so much!
شكراً جزيلاً 😎
working on a project that does this in some way right now :) the first thing I thought was, why use random noise when a latent vector would work so much better.
Great video! Thank you!
I will use autoencoder for speech watermarking I hope you will aborde this subject too and thank you for this awesome videos you are the best
Best explanation so far
Auto-encoding and de-noising seem to be equivalent to the processes we use for selectively listening to one speaker when in an environment where multiple members are speaking simultaneously.
can we do both of the applications at the same time ?
can you explain how background noise will be removed.
Good explanation. Thanks!!
Cool video!! I'm doing a research of speech to speech (voice convertion)
That sounds interesting!
can I make this project if I'm working on macbook m1 without a gpu?
nice video!!! thank you so much
Best video for autoencoders
Thank you, so helpful
can we implement VQ to audio classification with python?
Very Nice session. Would be grateful if you explain attention mechanism implication.
Thank you! I'll definitely cover attention / transformers in another series.
which loss metric would you use for the deep convolutional auto encoder? cross entropy or RMSE still?
RMSE / MSE are a good starting point here.
Great video 👍🏼
At the denoising part, do we always need to have same kind of noise in sound, for AE to denoise it ? For example, can samples with white noise and random noise denoised together ?
If you have enough samples for different types of noises, you should be good to go.
Thank you so much
awesome presentation! Subscribed
Thanks!
Nice job 👍🏼
can someone please explain how can i use auto encoders to filter out the background noise and reatain speaker voice ?
thank you so much
I can't find video about anomaly detection with AEs(
Thanks, can you cite your sources? (great explanation. thanks be)
Great informative video.
Thank you Nabeel!
Does anyone know how to find what are the important features that are stored in the latent space?
did u found a answer ?
ty :)
good
It's so cool how one idea like autoencoders can be used in audio, anomaly detection and I think even nlp as well! JVUVICFJ
You still didn't explain exactly how the autoencoder works!
Valerio, can you give me an email? has some important to say/
valerio@thesoundofai.com