Sequence-to-Sequence (seq2seq) Encoder-Decoder Neural Networks, Clearly Explained!!!

In this video, we introduce the basics of how Neural Networks translate one language, like English, to another, like Spanish. The ideas is to convert one sequence of things into another sequence of things, and thus, this type of neural network can be applied to all sort so of problems, including translating amino acids into 3-dimensional structures.

NOTE: This StatQuest assumes that you are already familiar with...
...and...

If you'd like to support StatQuest, please consider...
...or...

...buying my book, a study guide, a t-shirt or hoodie, or a song from the StatQuest store...

...or just donating to StatQuest!

Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter:

0:00 Awesome song and introduction
3:43 Building the Encoder
8:27 Building the Decoder
12:58 Training The Encoder-Decoder Model
14:40 My model vs the model from the original manuscript

#StatQuest #seq2seq #neuralnetwork

[tornadospin]

This channel is like the Khan Academy of neural networks, machine learning, and statistics. Truly remarkable explanations

[cat-a-lyst]

I literally searched everywhere and finally came across your channel. seems like gradient descent worked fine .

[reinerheiner]

This channel is gold. I remember how, for my first coding job, where I had no programming knowledge (lol) but had no choice than to take it anyways, I quickly had to learn php and mysql. To get myself started, I searched for the simplest php coding books and then got myself two books from the php & mysql for kids series, even though I was already in my mid twenties. Long story short, I quickly learned the basics, and did code for a living. Complex topics don't have to be complex, in fact they are always built on building blocks of simple concepts and can be explained and taught as such IMHO. Thank you so much for explaining it KISS style. Because once again, I have to learn machine learning more or less from scratch, but this time for my own personal projects.

[gabip]

I can't thank you enough for these tutorials on NLP. From the first tutorial related to RNNs to this tutorial, you explained so concisely and clearly notions that I have struggled and was scared to tackle for couple of weeks, due to the amount of papers/tutorials someone should read/watch in order to be up to date with the most recent advancement in NLP/ASR. You jump-started my journey and made it much more pleasant! Thank you so much!

[juliali]

It took me more than 16 minutes (the length of the video) to get what happens since I have to pause the video to think, but I should say it is very clearly explained! Love your video!!

[rachit]

An awesome video as always! Super excited for videos on attention, transformers and LLM. In the era of AI and ChatGPT, these are going to go viral, making this knowledge accessible to more people, explained in a much simpler manner.

[gilao]

Another great explanation!
It is so comforting to know that whatever I don't understand in class, I can always find a video in your channel and be confident that I will understand by the end.
Thank you!

[m.taufiqaffandi]

This is amazing. Can't wait for the Transormers tutorial to be released.

[ligezhang]

Wonderful tutorial! Studying on Statquest is really like a recursive process. I first search for transformers, then follow the links below all the way to RNN, and finally study backward all the way to the top! That is a really good learning experience thanks!

[paulk]

I just wanted to mention that I really love and appreciate you as well as your content. You have been an incredible inspiration for me and my friends to found our own start up im the realm of AI without any prior knowledge. Through your videos I was capable to get a basic overview about most of the important topics and to do my own research according to those outlines. So without taking into consideration if the start up fails or not, I am still great full for you and I guess the implications that I got out of your videos led to a path that will forever change my life. So thanks❤

[shafiullm]

I got my finals of my final course in my final day tomorrow of my undergraduate journey and you posted this exactly few hours ago.. thats a triple final bam for me

[harshmittal]

Hi Josh, I have a question at time stamp 11:54.

Why are we feeding the <EOS> token to the decoder, shouldn't we feed the <SOS> (start of sequence) token to initiate the translation?
Thank you for sharing these world-class tutorials for free :)

Cheers!

[mateuszsmendowski]

Coming from video about LSTMs. Again, the explanation is so smooth. Everything is perfectly discussed. I find it immersively useful to refresh my knowledge base. Respect!

[serkanbesim]

I genuinely love you for these videos holy smokes

[vicadegboye]

Once again, we can't appreciate you enough for the fantastic videos!

I'd love a clarification if you don't mind. At 8:44 - 8:48, you mentioned that the decoder has LSTMs which have 2 layers and each layer has 2 cells. But, in the image on the screen, I can only see 1 cell per layer. Is there something I'm missing?

Meanwhile, thanks a lot for replying on your videos. I was honored when you replied promptly to comments on your previous video. Looking forward to your response on this one.

[khaikit]

Hi Josh,

Thanks for the much-needed content on encoder-decoder! :)

However, I had a few questions/clarifications in mind:
1) Do the number of cells between each layer within the Encoder or Decoder be the same?
2) From the illustration of the model, the information from the second layer of the encoder will only flow to the second layer of the decoder. Is this understanding correct?
3) Building off from 2), does the number of cells from each layer of the Encoder have to be equal to the number of cells from each corresponding layer of the Decoder?
4) Do the number of layers between the decoder & encoder have to be the same?

I think my main problem is trying to visualise the model architecture and how the information flows if there are different numbers of cells/layers. Like how would an encoder with 3 layers and 2 cells per layer connect to the decoder that perhaps have only 1 layer but 3 cells.

[shahrozansari]

I like how your videos backpropogate so I have to watch all of them if I want to understand one.

[kmc]

I'm a student who studies in Korea. I love your video and I appreciate that you made these videos. Can I ask you when does the video about 'Transformers' upload? It'll be big help for me to study NLP. Thank you.

[ZinzinsIA]

Absolutely amazing as always, thank you so much. Can't wait for attention and transformers lessons, it will again help me so much for my current internship !

[diamondstep]

Love your videos Josh! Thanks for sharing all your knowledge in such a concise way.