-: So let's continue to dive down
this fascinating world of AI.
And now let's talk about transformer architecture.
So, no, it's not that kind of transformer.
Transformer architecture is a sophisticated type
of neural network architecture designed
to handle sequential data efficiently.
Making it particularly well suited
for tasks in natural language processing, aka, NLP.
So what does all this mean?
Well, let's dive in.
Natural language processing is a field of technology
that helps computers understand, interpret,
and respond to human language in a way that's usable.
It's like teaching computers to understand our language
and communicate with us using text or speech.
Through NLP, computers can read text,
hear speech, interpret it, even measure sentiment,
and determine which parts are important.
So whether it's Siri understanding
a question that you asked,
or Google translate turning English text into Spanish,
that's all NLP happening behind the scenes.
And because we're learning so many new words
that you may have not heard before,
I wanna use a fun example to explain
what a neural network like transformer architecture is.
So a neural network can be thought of like a team
of workers trying to solve a puzzle.
Each different type of team has different attributes
and different characteristics, which will make them excel
or fall short on certain types of puzzles,
just like in real life.
And the more puzzles a team tries to solve, of course,
the better their performance is gonna become.
So let's dive into the different layers of this example.
We first have our input layer,
which are gonna be the puzzle pieces.
So our puzzle pieces are the input data
that's gonna be handed out to the team of workers trying
to solve this puzzle, which is in this situation,
our neural network.
The team members, which for a neural network are equivalent
to the hidden layers are gonna work together,
passing around the pieces,
making connections based on the shape,
and the characteristics of the pieces.
These hidden layers of the neural network assign parameters
to the input data
and enable a neural network to learn complex patterns
and make predictions
or decisions based on
what it gathers from the input data.
Then we have our activation functions, aka,
the decision making process.
Each member of the team is making decisions
on whether or not a connection between pieces is correct,
aka, making the best conclusion possible
with all of the available pieces of information.
This decision making process is just like you sizing up
each little piece of a puzzle, the shape,
the specifics of it, the dimensions of it,
and going through and making the decisions
of where this piece would best fit
in the puzzle you're trying to solve.
Then we have our output layer,
which would be our finished puzzle.
So as the pieces fit together, the puzzle, aka,
the output starts to take shape.
And in the end,
the completed puzzle represents the solution,
aka, the output data to the original user's input.
And with this neural network,
the more puzzles that the team solves,
the better they get at finding connections faster
and more accurately,
which represents the main learning process
of a neural network.
So in real life,
you might have your own natural given skillset
that makes you really good at solving.
Word puzzles are really good at solving physical puzzles
like Rubik's Cube, and that natural tendency
and ability will make you excel at certain puzzles
or projects and make you not as efficient at other ones.
And neural networks are the same exact way.
So let's briefly talk about the different types
of neural networks.
So first up is transformer architecture.
This is the main one we've been talking about
that's used by GPT.
And because of the way it's designed,
it has the ability to excel with text-based data
due to its ability to quickly understand
and analyze the relationship between words.
No matter how far apart they are in a sentence,
in a paragraph, in a document, in an article,
it can take all of the given words from an entire input
and analyze the context
and how all of the words relate to each other.
Next, we have a convolution neural network or a CNN.
The main uses of this are image and video recognition
and overall image classification.
They're really good at this
because of their ability to scan through pictures,
focusing on small pieces at a time,
and identifying patterns and features like edges
or even colors, which help it in recognizing visual objects.
And then we have general adversarial networks.
The main use here is in generating new data
and content from a given data set.
These are commonly used in image generation, style transfer,
and sometimes even audio generation.
The adversarial network is like having two artists
in a competition.
One is trying to create a realistic piece of artwork,
while the other is judging if the artwork is real or fake.
And through this back and forth, both get better,
leading to creating and incredibly convincing
and realistic looking image.
Lastly, we have auto encoders.
Now there are many more different types of neural networks,
but the last one we'll mention are auto encoder.
The main use of these is data compression, denoising
and anomaly detection.
And this is all used for image, text
and sometimes audio processing.
So auto encoders are really a good fit
for finding patterns and reducing noise in data.
It does this by compressing it into simpler forms
and then expanding it back,
which can be helpful in tasks like spotting anomalies
or outliers in data,
or even organizing that data more efficiently.
Now that we have more of an understanding on these different
types of neural networks and a high view of how they work,
now let's talk about
how transformer architecture specifically does its job.
Within the transformer architecture,
there's an encoder which reads
and processes a sequence of data, just like a sentence.
And this can create a short summary
that captures the important information in that sentence.
The decoder then takes this summary
and generates a new sequence of data, for instance,
a translated sentence based on it.
Throughout this process, both the encoder
and decoder pay attention to different parts
of the input data to understand the relationships
between words and elements,
making sure that the output is meaningful
and relevant to the input.
So again, you know, I love my metaphors,
and because this is such a complex topic
that we're synthesizing into a video lesson,
let's hop into this next metaphor.
So we have our transformer architecture.
Now imagine a bustling and busy airport
where airplanes from various places
are landing and taking off.
In this metaphor,
the transformer architecture is like the entire
airport operations.
The encoder is the arrival terminal,
and the decoder is the departure terminal.
Let's dive into this more.
So when planes are arriving at our airport,
our planes are gonna be all of our data
and our airport is gonna be our transformer architecture.
They all have to enter first through our arrival terminal,
which is our encoder.
At this arrival terminal,
each plane's details, aka, individual pieces
of data are logged, collected, and checked.
These passengers of the plane, aka, the features
and specifics of the data are screened and processed.
This terminal has several counters
and different security checks, which would be layers
of the encoder to understand who
and what are all arriving from the airplane,
aka, the original data.
The personnel who work at the airport
is something called the self attention mechanism.
And their job is to work at the terminal,
paying attention to different groups of passengers,
aka, different parts of the data based
on their specific needs, destinations,
and other important criteria.
By the time all the planes
and passengers are processed,
the arrival terminal now has a comprehensive understanding,
which we call a high dimensional representation
of who and exactly what has arrived
and their respective details.
Now, all of those passengers that arrive need
to go somewhere through the departure terminal.
So these passengers need to, of course, board new planes,
aka, the generated output to get to their destinations.
So they move to the departure terminal,
which in this case is our decoder.
At the departure terminal,
there are several different counters
and security checks, which are layers of the decoder
to ensure that everyone's getting to the right plane
with their correct baggage and information,
all their belongings, and all of their details.
On top of that, there's a special team that coordinates
with the arrival terminal
to get insights about any special requirements
or conditions of the passenger
to just ensure a smooth transition.
The personnel through this process,
which is our self attention mechanism, is in this terminal,
also paying attention to the different groups of passengers,
again, ensuring they board the right planes
at the right time.
Considering their previous and upcoming journeys,
aka, the context of the input.
Once this very complex
and multi-layered process is complete,
the planes, aka, our output take off
for the departure terminal,
and they carry our passengers, which is our generated output
to their correct destinations.
Based on all the information
that was processed from the moment they arrived,
and all the coordination of information
through the entire airport.
So with all that said,
the transformers encoder processes and understands
the input data, which is what you type in,
which the decoder generates the output data,
which is the answer that you receive.
Each with its layers of self attention mechanism,
ensuring that all of the right amount of details,
context, nuances, and subtleties are being collected
and correctly processed through each step of the way.