Attention is all you need explained

Attention is all you need. Welcome to Part 4 of our series on Transformers and GPT, where we dive deep into self-attention and language processing! In this video Lucidate will guide you through the innovations of the transformer architecture, its attention mechanism, and how it has revolutionized natural language processing tasks.

In this video, we cover:

-The introduction of self-attention in transformers
-The limitations of Recurrent Neural Networks (RNNs) and how transformers address them
-The role of Query, Key, and Value matrices in attention mechanisms
-The backpropagation process for training transformer models

This video is perfect for anyone interested in understanding the inner workings of transformer language models like ChatGPT, GPT-3 & GPT-4. Don't forget to check out the previous videos in the series to get a complete understanding of the topic.

If you find this video helpful, make sure to like, comment, and subscribe for more informative content on AI, machine learning, and transformers.

Stay connected with us on social media for updates and more exciting content:

#Transformers #GPT3 #SelfAttention #LanguageProcessing #MachineLearning #AI #Lucidate

Attention is all you need. Transformers like GPT-3 and ChatGPT (as well as BERT and BARD) are incredibly powerful language processing models. They are able to translate, summarise and compose entire articles from prompts. What is the magic, the 'secret-sauce' that gives them these capabilities.

In this video we discuss the transformer architecture and its key innovation, self-attention, which allows the model to selectively choose which parts of the input to pay attention to.

We explain why we need attention and how transformers use three matrices - Query (Q), Key (K) and Value (V) - to calculate attention scores.

We also explains how back propagation is used to update the weights of these matrices.

Finally, we uses a detective analogy to describe how transformers focus on the most relevant information at each step and better handle input sequences of varying lengths.

Stay tuned for the next video where we take a deeper dive into the transformer architecture.

=========================================================================
Link to introductory series on Neural networks:

Link to intro video on 'Backpropagation':

=========================================================================
Transformers are a type of artificial intelligence (AI) used for natural language processing (NLP) tasks, such as translation and summarisation. They were introduced in 2017 by Google researchers, who sought to address the limitations of recurrent neural networks (RNNs), which had traditionally been used for NLP tasks. RNNs had difficulty parallelizing, and tended to suffer from the vanishing/exploding gradient problem, making it difficult to train them with long input sequences.

Transformers address these limitations by using self-attention, a mechanism which allows the model to selectively choose which parts of the input to pay attention to. This makes the model much easier to parallelize and eliminates the vanishing/exploding gradient problem.

Self-attention works by weighting the importance of different parts of the input, allowing the AI to focus on the most relevant information and better handle input sequences of varying lengths. This is accomplished through three matrices: Query (Q), Key (K) and Value (V). The Query matrix can be interpreted as the word for which attention is being calculated, while the Key matrix can be interpreted as the word to which attention is paid. The eigenvalues and eigenvectors of these matrices tend to be similar, and the product of these two matrices gives the attention score.

The Value matrix then rates the relevance of the pairs of words that make up each attention score to the ‘correct’ word that the network is shown during training.

By using self-attention, transformers are able to focus on the most relevant information, helping them to better handle input sequences of varying lengths. This, along with the semantic and positional encodings, is what enables transformers to deliver their impressive performance. In the next video, we will take a deeper dive into the transformer architecture to look at examples of training and inference of transformer language models.

=====================================================================

#ai #artificialintelligence #deeplearning #chatgpt #gpt3 #neuralnetworks #attention #attentionisallyouneed

[christopheryeung]

Amazing video and out of all videos I have seen so far, I think the analogy really helps break down the complex mathematical relationships into more relatable concepts. I particularly like this one and the one where you explain positional encoding like how a clock has hour and minute hands.

[jazonsamillano]

Richard Walker, you are the greatest! The work you put in to these videos are mind-boggling! They are worth watching again and again. Once you turn on the Super Thanks buttons on these videos, I will pay to watch each one! That's how high quality they are. You are doing the entire NLP world a remarkable service, Sir!

[deepaksingh]

The visualization used to explain concepts is just awesome..it really makes learning concepts very easy.

[nangld]

This is probably the best explanation around. Other explanation don't even mention that Key, Query and Value are matrices and what interpretation their values hold.

[ascensionunlimited]

I'm making it a personal goal to watch every single one of your videos.. at least once

[stevenkies]

A fantastic series. You deserve 1 million+ subscribers!

[aiartrelaxation]

What a fantastic presentation 😍😍😍😍 Everyone learns different, I am learning best with visual. Thanks again

[webinnovationspartners]

One of the best explanations of this topic. Great video. Thanks.

[manuelrodriguez]

Your videos are really helpful, thank you very much!

[blaketurner]

Really awesome and informative content. Slight information overload at times but nothing a quick pause to better digest the information due to the excellent supporting graphics and text can’t fix. Definitely need to spend some time exploring your previous videos on these topics.

I was watching this after Andrej Karpathy's video about how to create a GPT like transformer with PyTorch and I'm finally able to understand a bit better what these Q, K, V values are for. It's mind-blowing really that you can force structures like this to emerges from a dataset just by defining this architecture. I wonder how they come up with it, how much it was experimenting and sheer luck. :) I would love to see somehow how a neural net like this fires when the next word is predicted, but I guess there's no easy way of visualizing it as the dimensionality is insanely high, and even if we could, understanding the connections would be near impossible.

[lucarappez]

Thank you Luci for this wonderful explanation!

[aiandblockchain]

Good graphics, thank you for your explanations!

[signupisannoying]

Really really great explanation. Thank you very much. Subscribed.

[krishnaraj-dsme]

Thanks a lot for beautiful video . Thanks

[apoctapus]

I’m learning so much from you. The whole style is great. You are obviously comfortable with the material and I’m sure others have mentioned it, but adding a second or so pause here and there between concepts would help old folks like me retain your lesson’s concepts better because we have a small bookend/break/pause/gap. Primacy and regency I think is what my partner called it.

Did you create your diagrams in the same tool you use for your financial graph animations?

[billvvoods]

Very good video. Thanks for helping us.

[simongardner]

Very good tutorial. Normally on YouTube they are too high level and black box, or too low level and mathematically complicated for a beginner. The pace is not too fast either. This is just right, although I think I need to go back and watch some of the earlier tutorials, as I may be starting in the middle.

[phanihunt]

This guy's video made me install python git vs and now I need to get pinecone


Thanks a ton, from a non techie.

[botable]

Impressive video and animations! There is a lot of good content in there. One small piece of feedback: The animations were impressive and certainly helped in many places, however at points there were so many animations active and they were switching so rapidly, that I found it was actually distracting from the message. My personal preference would have been for less rapid transitions and more time spent on each animation so there was more time to concentrate on the topic being discussed. Everyone is different though... so it could just be me. Thanks again for creating.