Once we've studied earnings we can go even deeper into how deep learning can be used on text as the
next course natural language processing with deep learning in Python or deep learning in Python.
Part 6 in this course.
We look at a very important concept called a word in buildings.
These allow us to turn words which are categorical variables into vectors which are numbers that a neuron
that we can read.
This is why it's very important to have studied unsupervised learning before this.
Because finding word embedding is actually an unsupervised task.
Word embedding also allow us to make use of pre training which was discussed in deep learning in Python.
Part 4 in this course we also look at a very advanced model for doing sentiment analysis called a recursive
neural network or a tree neural network.
This is an example of a dynamic neural network because it changes its structure based on what input
you give it.
Most Deep Learning libraries are not equipped to handle dynamic neural networks and I demonstrate what
happens if you try to build one the naive way.
You basically end up building a separate neural network for each of your training samples and it's going
to eat up all your RAM and make your computer slow down to a crawl.
So you get a firsthand perspective of why working with dynamic neural networks is not easy.
But luckily we are able to make use of our knowledge of recurrent neural networks and remember that's
the prerequisite to this course in order to convert a tree into a sequence which in Arnon is capable
of handling.
So that's why ends is a prerequisite to this course now you'll notice that this course deep in OPI has
no outgoing edges.
This is because it's the most advanced course that I have on this path.
For now I certainly plan on expanding in this direction in the future.
However this is not the last deep learning course in the series.
After this we have deep reinforcement learning which is deep learning in Python part 7.
With that said I think now is a good time to go back and start exploring the reinforcement learning
path so you'll notice that I have Bayesian machine learning feeding into reinforcement learning on the
surface.
These two courses might seem unrelated but there is a very important concept you'll learn that applies
to both called the explore exploit dilemma in Bayesian machine learning.
You'll learn this idea in the context of trying to optimize a click through rate or conversion rate
or in other words the number of times people buy things from your website versus the number of times
people visit at your Web site.
Very practical concept.
I think if you do anything related to e-commerce now in reinforcement learning we look at the exploit
exploit dilemma again.
But in the context of playing games reinforcement learning is like a third branch of machine learning
whereas the other two are supervised and unsupervised learning.
The main difference is that supervised and unsupervised learning look at static data and reinforcement
learning.
The idea is more like you have a robot living in the real world.
It can take the experiences it had today and based on them behave more intelligently tomorrow.
So the learning paradigm and reinforcement learning is sequential.
This is opposed to supervised and unsupervised learning where your dataset usually resides in some file
on your harddrive so in reinforcement learning part 1 We get all the basics as you might expect.
This is a prerequisite to deep reinforcement learning since deep reinforcement learning applies those
concepts to more difficult games but you'll notice that this is not the only prerequisite to deep reinforcement
learning as the title suggests.
This is also dependent on knowledge of deep learning in particular convolution or neural networks but
as we know in order to build a CNN we have to know how to build a regular neural network which means
we have to know what a neural network is and why it's useful and so on.
So in deep reinforcement learning these two paths converge you combine your knowledge of both reinforcement
learning and deep learning in this course.
Now the reason it depends on CNN and not say aren't ends which is over here is because we'll be learning
to play visual games.
So for example we can learn how to play a video game like Pong a breakout which are classic Atari games
from the old days.
And so those are images because they're basically screenshots from the screen.
In the future we might end up applying art ends in which case our own ends will become a prerequisite
to that course so that's the end of the reinforcement learning path.
For now I'm very excited to bring you more updates in this area in the future Let's now jump back to
logistic regression where we can see another outgoing age to supervise machine learning.
So why is this edge here.
Well you may recall that linear regression and logistic regression are both linear models that do regression
and classification respectively.
These are both supervised learning tasks.
And so it makes sense that now that you know one model for regression and one model for classification
it's time to dig deeper into supervised learning.
The thing with linear regression and logistic regression is that they aren't really different models.
They are both just the line because they do different tests the techniques and interpretations are slightly
different though and there are of course different models that are not linear models that can do these
tasks.
And that's what this course is all about in this course.
We look at classic supervised machine learning techniques like Kenya's neighbor decision trees the perception
and the base classifier much like how logistic regression was the basic building block of the neural
network.
Classic models like decision trees are the basic building block of ensemble methods so that's why this
course is a prerequisite to ensemble machine learning.
Again we use the same logo with a different color to signify that these two courses are very closely
related in ensemble machine learning.
We learn how to combine multiple decision trees in different ways in order to make some very powerful
classifiers.
What's really remarkable about these methods is that they are very easy to plug and play on data.
So if you're looking for a plug and play solution without having to learn a lot of theory then deep
learning is most likely not for you but ensemble methods are a great fit deep learning is very dependent
on hyper parameters.
And if you choose incorrectly your model will perform very poorly.
Sometimes it requires immense computing power to find good hyper parameters.
This is an active area of research.
It has not yet solved.
This is why you can implement what you see in a deplaning paper.
But suppose the author left out some seemingly insignificant detail.
So you end up having to make an assumption and then your results end up totally different.
So deep learning is fragile.
But luckily ensemble methods are not.
We focus on two very famous ensemble methods be random forest and a boost.
So that's everything on these supervised machine learning track.
For now Next we see an edge going from supervised machine learning to unsupervised machine learning
in particular cluster analysis.
The reason we study supervised learning before unsupervised learning is because unsupervised learning
is a little more abstract.
It takes more effort on the student's part to realize why it's practical and what it can be used for
cluster analysis shows us how to model data that does not come with targets as you might guess.
We do this in the form of clustering the idea behind clustering is very simple.
We want to know how many naturally occurring groups of data there are and what are the relationships
between the data in these clusters.
So for example if you were clustering books you might find a book about Steve Jobs and a book about
Elon Musk in the same cluster.
This cluster is probably about tech companies in Silicon Valley but you don't need a label in your data
to tell you them you can discover it yourself by looking at how the data naturally groups together now
as I mentioned earlier I think once you learn about both supervised and unsupervised learning you'll
be ready to jump into reinforcement learning.
I haven't made cluster analysis a prerequisite to reinforcement learning since none of the material
depends on this course.
But it's good to know about these techniques so that you have a more mature and experienced view on
machine learning.
What is sort of a sequel to cluster analysis is Hidden Markov models.
The reason might not be clearer first.
So let me give you two reasons.
Number one they're both unsupervised machine learning models just that H.M. MS is harder.
So it's natural to learn about clustering first clustering is also about static data whereas a.k.a.
memes are about sequences.
So it's similar to the process we did in deep learning.
We looked at static data like images then sequential data like text reason number two in cluster analysis.
We learn about a technique called the Ghazi and mixer model which we make use of in the H M M course.
One key point is they both learn by using the expectation maximization algorithm.
So it's good to first see the Eon algorithm on a simple model and then when you see them again on a
more complicated model like the H M M it won't be as intimidating one key concept you learned in eight
members is the mark of assumption that just means the current state depends only on the previous state
but not any state before.
This is a simplifying assumption that usually makes the math easier to work with you will also notice
that we encounter the Markov assumption in reinforcement learning.
However it's not too hard to learn it from scratch.
And so for that reason I do not consider H amounts to be a prerequisite to reinforcement learning the
Markov assumption is really the only thing they have in common.
There is also a slight connection between cluster analysis and unsupervised deep learning.
So I'm not going to draw the link right now but I sometimes consider this to be unsupervised machining
part one and this to be unsupervised machine learning part 2.
We also see that each imams feeds into the aunt course which is about deep learning so why might that
be.
This is of course because both these models are models that can learn about sequences in particular
in both these courses we model text as sequences but whereas the H M M makes use of the mark of assumption
the RNA does not enhance the N is a more powerful model and so this just goes along with the main theme
that we always go from simple basic models to more complex models.
This is also something you should do in your work as well.
If you start with a simple model you often find that it is faster and more robust complex models sometimes
break down but they are also more difficult to implement and might not even be fast enough.
Of course that's just a generalization.
So you always want to analyze engineering tradeoffs individually for every problem you have.
But there is one last link in this part of the graph here that I want to explain.
And that's the first because you can see that it depends on supervised machine learning and feeds into
deep end up.
The main purpose of this basic an AP course is to apply basic machine learning models to text.
So that's why supervised machine learning comes before it is because that this course was all about
basic machine learning models.
The important skill for MLP was not the implementation of those models but rather a bigger picture perspective
on how machine learning is used.
What is the interface between the data and the model.
What does the model do.
What is its input an output.
How is the output interpreted.
And so we take those principles and we apply them to text in this way we can see that text can be treated
in such a way that you don't have to think about it any differently than any other data this reinforces
the principle that all data is the same.
The machine learning model doesn't care what your data is.
All it sees is a table of numbers.
It doesn't care if it's text or images or radar signals from space.
The model just does what it was designed to do on the numbers that you give it.
So this course gives you a high level systems perspective on working with machine learning models in
text.
This easy A.P. course also feeds into deep entropy which is of course not so easy because it depends
on a lot of background and deep learning one of the main questions I get in the MLP course is how do
I improve the results of these basic models.
And a lot of the time the answer to that is well you have to use a more complex model but of course
that necessitates learning how that complex model works and deep NRP is an example of that because we
learn a state of the art method for sentiment analysis whereas an easy A.P. we used only a linear model.
So it's important to understand that while yes it's possible to improve the predictive ability on simple
basic models as you can see it's not always an easy path to get there.
So you have to make sure you're prepared.
Case in point.
Just look at all the time spent just to get too deep in IP.
It's not an easy task let's now scroll over to Ganz and variation all auto encoders just like how deep
reinforcement learning is not related to deep end LP.
This isn't really related to deep reinforcement learning either.
This is deep learning part 8 by order of creation only it is these spiritual sequel to unsupervised
deep learning which was deep learning in Python part 4.
So just to reiterate this is part 6 part 7 and part 8.
By order only but they are not related to each other conceptually although it's always nice to know
all these things because more context makes future things easier to learn so the reason this is linked
to this is because it gains and variation all auto encoders are also unsupervised deep learning models
but whereas unsupervised deep learning was all about how to improve supervised learning gains and variation
of auto and coders don't have any direct benefit to supervised learning at all although we do make use
of supervised learning within the course in this course.
The focus is on generating images we've seen that again can create photo realistic images based on a
do on their own network system.
That's pretty cool because before games we didn't have any kind of machine learning model that could
generate real looking images.
Nowadays Ganz are able to generate high resolution high quality images of people that you can't even
tell are not real people.
It certainly makes the idea of the matrix seem very possible.
All right so I hope you found this lecture helpful.
We saw that these courses are related to each other in some pretty complicated ways learning machine
learning is not exactly linear.
Sometimes you have to take a one course before you can take the next sometimes one course might just
be related to another course by some key concept but maybe in one context it's a lot easier to understand.
So remember to keep in mind that these arrows did not all indicate strong prerequisites or rather there's
just a relationship between the two courses.
I hope that this lecture explained any nuances between what is a prerequisite and what is not and I
hope I did a good job of answering which order should you take these courses in and why.