Introduction To Autoencoders In Machine Learning.

Autoencoders are neural networks designed in a way they can learn any existing structure in a dataset. They create a compact representation of the data we can leverage later in different applications.
Some applications where you can leverage autoencoders: anomaly detection, image denoising, information retrieval, imputation, feature extraction, and dimensionality reduction problems.
Convolutional autoencoder for image denoising example: keras.io/examples/vision/auto...
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[@theDrewDag]

One of the best and most intuitive explanations of what an autoencoder is. You are a great teacher!

[@underfitted]

Wow, thanks!

[@ShawhinTalebi]

This is my first time coming across using autoencoders for anomaly detection. Super clever! Thanks for sharing (and recording this twice 😂)

[@ballerzhighlights4328]

What a teacher!!! Amazing!

[@underfitted]

Thanks!

[@atlasflare7824]

Great abstraction of how autoencoders (as a wonderful idea) works. Thank you, and please carry on what you are doing.

[@PritishMishra]

This was amazing! Even I am a huge fan of Autoencoders it is one of the most amazing thing in Deep Learning.

[@123arskas]

Thank you. The pictures you printed and sketched were awesome. Appreciate the simplicity of this complex topic. Wish there was a full video of Handling Missing Data with complete intuition because we can't be experts of all fields and thus deciding whether to drop, impute etc a column is right or wrong on what situation. I liked the new column option in your short video so the ML model can at least get the information for what we're dropping. Same goes for Outlier detection and removal techniques.

[@underfitted]

Greatest thing ever!

[@paulallen1597]

Really clear and accessible description of Autoencoders. Thank you for putting all the time and effort into this. Looking forward to more.

[@underfitted]

You're very welcome! More is coming!

[@Param3021]

I just started learning about Neural Networks and today you showed me a different way to use Neural Networks as Auto Encoders. I like the denoising images one, cuz it's different from other (shark) classification problem. The way you teach about a topic is really amazing sir. 💛

[@underfitted]

Glad I could help!

[@rohithk8832]

Great video, I was trying to understand auto encoders for a long time, all the other videos had just the keywords but your scenario based explanation made it clear and understandable. Thank you

[@FelLoss0]

You're the greatest KZbinr I've ever come across since I started my ML and DNN self-taught journey. Please keep on creating more of this top notch content. You have a person here who leaves this video with a huge smile on the face because everything was crystal clear. Greetings from a PhD student in Finland!

[@waylonbarrett3456]

I've built an AGI which is very loosely inspired by how an autoencoder functions. It's an abstraction of the essence of this same idea.

[@AlexRoxjar]

This is a great explanation. Simple, clear, not too long.

[@underfitted]

Glad you think so!

[@piyushborse]

Your videos are insanly Good.

[@underfitted]

Thanks

[@samreiter4707]

OMG! this is amazing! going to be tacking this onto the front of some of my personal NNs! thank you for the inspirations!

[@underfitted]

You are so welcome!

[@MrAhsan99]

Thanks, Santiago for putting so much effort into your videos.

[@underfitted]

Glad you like them! I don't, so I'm trying to improve. So many things I want to do better!

[@niloofarzarghampour9392]

Amazing video!

[@dhemakumar1594]

This is such a nice explanation of autoencoders. Thank you!

[@underfitted]

You're very welcome!

[@francouys4389]

Very entertaining! brilliant explanation

[@underfitted]

Glad you liked it!

[@mtomazza]

Great! Following on Twitter and now here!

[@underfitted]

Thanks, Jairo!

[@SamDig]

This is Amazing; such an intuitive explanation. Thank you

[@underfitted]

You're very welcome

[@osanebiemmanuel5041]

Thank you so much for this video, really great example, i just learnt about autoencoders today, i would be looking forward to more of your uploads

[@underfitted]

Glad it was helpful! More is coming!

[@leonkepler7036]

This video is interesting, easy-to-follow, and informative. Than you.

[@underfitted]

Thanks!

[@itsm0saan]

Amazing work! I appreciate the amout of work being put to beak down the concepts! I wish If we can have a video about EDA, that'll be amazing as well. Thanks!

[@underfitted]

Great suggestion!

[@thevoyager7675]

You are greaaaaat man!!!!

[@ElijahEchekwu]

Thanks, Great explanation

[@underfitted]

You are welcome!

[@dimasveliz6745]

Awesome !

[@underfitted]

Thanks!

[@kemalariboga]

Sir, you are just great. I want to ask whether this approach would be helpful to auto-label the images that contain different objects. Thank you!

[@kapendification]

brilliant!

[@underfitted]

Thanks

[@Levy957]

You're amazing, im studying AutoEncoders

[@underfitted]

Thanks so much!

[@underfitted]

Good

[@ian-haggerty]

[@underfitted]

Thanks!

[@ian-haggerty]

​@@underfitted As an introduction to autoencoders, this has definitely got me excited! Can't wait to get hands on with this. Use cases are insane! It's relevance to human-level pedagogy is also fascinating.

[@nedafiroz514]

What would you recommend, can we use autoencoders for feature extraction from the text data for sentiment analysis? Would it work? I mean the idea overall , is it suitable or is it just a bad idea

[@hanweiz84]

I am now thinking of using Autoencoders on anomaly detection on sensor values of IoT systems. Do you think that will work?

[@underfitted]

I don't see why not.

[@user-ny8xt1ui3h]

When will the new video of the autoencoders without bottleneck be released, please?

[@maxprofane]

You should change the name of the channel from Underfitted to Underrated for accuracy.

[@underfitted]

Lol. Thanks!

[@zocti]

Cacetada, senti até minha mente expandindo ao infinito

[@underfitted]

Lol

[@esra_erimez]

Came for the sharks, stayed for the autoencoders

[@underfitted]

Thanks, Esra!

[@mizoru_]

Ooh, so you're the guy behind bnomial

[@underfitted]

That would be me, and a friend.

[@willyouwright]

It seems it would be more efficient for an auto encoder to look for at least 500 key patterns in an image.. i.e. deep, shallow, dark, light, school of fish, ect ect.. Need like a contextual autoencoder.. then the ML can answer questions better and need much less training.. we dont want to overfit data, And we dont want to train with allot of data.. we also want ML to learn on the fly.. We need it to be able to tell the difference between friend or foe.. fake or real.. unless it can do this, its very limited.. You can solve fakes by looking for evidence of authenticity and cross checking across many domains.. i.e. you dont feed a horse petrol..

[@hasanx8317]

You should've used an AI model to de-blur the original video :D

[@underfitted]

Lol

[@esra_erimez]

Wait, no picture of a sharkyoctopus??? I'm disappointed!!!

[@underfitted]

I tried… I promise 🫣

[@Giraffinator]

Help, I don't know why I'm here or why I watched the whole thing, i need an adult

[@underfitted]

Lol