So in this lecture, we'll be transitioning into the next part of the course, which is on deep learning
and deep neural networks.
The previous part on machine learning is now complete.
The goal of this section is pretty simple.
You'll learn about deep neural networks, which will allow us to remove many of the simplifying assumptions
we used earlier in this course.
For instance, instead of bag of words techniques like TF IDF, we'll use embeddings instead of the
mark of assumption.
We simply won't make any assumptions about how many passwords can influence the next word in a sentence.
Instead of using a linear models like SVD to transform our data insulating factors, we can use nonlinear
neural networks, which are much more powerful.
So the outline for this module is very simple.
We'll start with learning the basic syntax of TensorFlow and how it can be used to build machines that
learn.
We'll do this by first using linear models, which in the study of neural networks are a model of the
neuron.
If you don't have a background in biology, all you have to know is that the neuron is a cell in your
brain, and you have many of these sitting inside your head which do things like process information
and make decisions about what you should do with yourself.
Basically, everything you feel, every thought you have and every action you take is because of your
neurons.
It turns out that this a neuron model is the same as logistic regression, which, as you may recall,
we've already studied.
So this will be a new perspective on that model and obviously a new way to implement one as well.
Once we've learned about the basics of neurons and TensorFlow syntax, we'll move on to artificial neural
networks.
This is one we'll also learn about embeddings.
Embeddings are a better method of putting text into neural networks compared to simple bag of words
models like Tia Faria F.
Once we've learned about Ian's will then learn about CNN's or convolutional neural networks.
These were originally invented for images, but as you'll see, they can be used for any kind of continuous
signal, which is what we get when we convert our text into embeddings.
Basically, we learn how to convert text into a multi-dimensional time series.
The third section will cover recurrent neural networks, or aren't ends, which are neural networks
specifically designed with sequences in mind.
We'll look at the most popular variants of our own ends, like the Elysium and the Gear you.
So the final thing I want to mention in this lecture is the strategy you should take if you've already
studied TensorFlow or deep learning with me in the past.
Obviously, the theory behind deep learning hasn't changed.
So there's nothing new to learn about in that regard.
If you already know about Annan's CNN's inheritance, either from me or from somewhere else, it is
safe to simply skip to the code preparation or even just the code lectures themselves.
So you might also be wondering what's the difference between taking this course and of course, I've
made which actually focuses on deep learning.
The answer should make pretty logical sense in my deep learning courses.
You learn more about various aspects of deep learning, like why and how neural networks work.
One major part of this is the different types of laws functions in why we use them, and also the exact
details of optimization methods like Adam A.G D..
This course will not go over those because if you want to learn the intricate details about deep learning,
well, then you should take a deep learning course.
On the other hand, a generic deep learning course will not focus on NLP, which this course obviously
does.
So although you can apply neural networks to all kinds of things like disease prediction, fraud detection,
images, time series and so forth, we will only apply them to NLP.
So you can think of this like a Venn diagram where there's a little bit of crossover, but neither is
a superset of the other.
This course will focus on some parts of deep learning, while the deep learning course, which I've
taught will only cover some of NLP.
Essentially in this course, we are mostly concerned with how to apply deep learning to NLP.
So an example of a topic that we should not care about is why the cross entropy loss is the right laws
for classifying text.
And this course, I'm simply going to tell you that this is the case.
And if you want to study deep learning more in depth to learn about why I have left resources for you
in the GitHub repo.