Hello, everyone, and welcome to our beginner's neural network master class with Tenzer Flow J.S. In
this lecture, we are doing an overview of what you will learn in this course.
We're going to overview topics that will cover and the projects that you'll build.
This is a very projects heavy course.
We're going to be building about nine projects together.
Let's get started with what we're covering first.
First off, we're going to introduce JavaScript.
So this is a prerequisite that will include in the course in case you don't have any programming experience
because we're teaching Tensor flogs, you have to have experience with JavaScript.
So that's why we're including this prerequisite introduction to JavaScript.
Other than knowing JavaScript, you don't need anything else for the course besides a code editor.
So that means a code editor like Adam or Brackett's, even a text editor or visual studio or even an
online code editor like Code Pen or Jaspin.
After we introduce JavaScript and how to program in JavaScript, we are going to jump into an introduction
to Tensor flowcharts.
We're going to cover what is machine learning, what is the Tensor Flow library?
And we'll also show you how to load in tensor flow into your website.
Following that, we're building our first neural network project, and in this project we're going to
build the components of a neural network.
First, we'll cover what is deep learning.
What is a neural network in the first place.
Then we'll learn how to build a Perceptron or neuron, how to build sigmoid functions, how to build
activation functions like a Relu and leaky relu.
And don't worry, if none of these terms makes sense to you now, they will make sense to you when we
get there.
So this is our first project building neural network components.
Then we are going to build a simple neural network where we're going to build the layers of the neural
network and then train and test the neural network.
And with Tensor flogs, we'll be able to run our neural network right in our browser and our website.
After that, our next project is going to be to build a neural network with cross validation.
First of all, introduce what is cross-validation.
Then we'll build a data set, we will build a neural network, will train the model and we'll make a
prediction with the model following that, our next project is very its image classification with a
neural network.
So this time, instead of building a neural network from scratch or building a neural network with Tensor
flogs, we're actually going to load a pre-built open source neural network and then we're going to
use that neural network to classify images on our website.
Following that, our next project is going to be to use recurrent neural networks with sensors flowcharts.
So again, we're going to load a neural network into our website that's pre-built.
And this time it's going to be able to type recurrent neural network.
Then we're going to set up our canvas and we're going to use the neural network to build a drawing.
So we're going to draw with an algorithm.
This is another very fun project because we're going to be able to quite easily load in a neural network
and get it drawing because it's pre-built.
This way.
You have experience with building your own neural networks to understand the fundamental components
and how they all work.
And then you also know how to use premade neural networks on top of that.
Next, our project is going to be to build a neural network for the X or algorithm.
If you're familiar with the coding interview, you've heard of the X or algorithm, it's not as hard
or confusing as it sounds, but the X or algorithm is commonly asked at the coding interview.
And we're going to learn and know that work that will have to build and learn the X algorithm on its
own, given a data set.
So we'll build a data set, will build a neural network, and then we will train our neural network
to learn the X or algorithm.
Following that, our next project, very fun.
We're going to learn how to detect objects in images with a neural network.
So we're going to have to load images into our website.
Then we'll have to load a pre-built open source neural network specifically created for object detection.
And then we're going to use the canvas to actually outline the objects in the image.
So the neural network will be able to find the positions of the objects and we will drop boxes around
those objects.
Following that, our next project is going to be to build another network from scratch with gradient
descent.
So we're going back into building our own neural networks from scratch instead of using pre-built neural
networks.
And this will help us understand a fundamental concept of machine learning known as gradient descent,
very important and very commonly used.
So we're going to build a project from scratch to really help you understand gradient descent.
We'll learn how to reduce a neural networks errors step by step manually.
But then we'll also learn how to use gradient descent to reduce a neural networks error and therefore
make more accurate neural networks with more efficiency on our on our end.
And our final project is going to be to build a deep neural network with back propagation.
So this time we're building a more complex network type because no one is going to be deep's or have
many layers and multiple features in the inputs.
And we're also including back propagation, which means the neural network will feed information forward
and back.
This is very important.
It's another type of neural network.
We are going to learn how to build a deep neural network with gradient descent from scratch.
Then we will train it on our data set.
Then we will incorporate Tensor flogs.
So we'll first, we'll build it from scratch, then we'll use centrifuges to make things more efficient
and then we'll incorporate back propagation on top of the deep neural network.
All right.
And those are the projects we will build in this course.
We are very excited to take you on this journey of learning how to build beginner neural networks intensive
flow flogs.
Join us in the next section.
We're going to jump into the prerequisite introduction to JavaScript in case you have no programming
experience, head on over to that next section.