Hello.
Welcome back.
And this lesson I'm going to give you a road map of the course.
In other words we're going to see the um the steps and the sequence of steps that we shall take in order
to be able to deploy state of the art models on our microcontroller.
So the course the first practical section is about the building blocks of neural network.
And in this section the court is going to be written in C language just to show you how we can build
simple neural network into the various building blocks from a C language perspective.
Then we are going to be building complete modules over here.
We're going to use Python cause if you're building neural networks in the real world there is a 99 percent
chance that you're going to be using Python.
So over here we'll see how to build our models completely in Python.
So I've added a section for those of you who are not familiar with Python.
I have other Python courses I've taken lessons from that course known as getting started with Python
or Python essentials.
And if you're new to Python when you complete that section you should be able to write Python code.
So we're going to build complete modules in Python then we going to use other libraries popular libraries
such as Caris and tensor flu.
We're going to be built in modules which carries intensive flu because in the real world when you're
building models you are going to use these libraries as well.
Once that is done we are going to see how to quantized the models that we build use in terms of flu
use in terms of flu for our embedded device in order to be able to deploy our tensor flu and care US
models.
We need to quantized them so that they can fit into the memory of our microcontroller.
We're going to see how to do that.
So that's going to be one step we would take and then we'll see how to deploy tens of flu models on
our microcontroller and over here we're going to be deploying the models and see could we we build the
neural network in Python and then once the neural network is built and we train it we get a weight with
quantized a weight and then we come to a microcontroller and we write C code to perform what is known
as inference so we deploy our tensor flow models on our our microcontroller and over here we're going
to be applying C code.
Next we'll be deploying models using in DeKalb mixed to a.
This is a library built by SD microelectronics for machine learning on SD microelectronics devices.
So we're going to see how to be deploying models using these uh this library as well.
And then we're going to be building models using the coffee framework.
The reason we'll be using the coffee is that the cafés the the framework best suited for use in these
arm seems this new network library so arm has a library known as CMBS and then in that it was seems
this neural network in the models that the library works perfectly with all the models.
The framework that a library is easily workable with is the coffee framework which is a very simple
framework for building your network.
We're gonna get acquainted with coffee and build someone else there and then cry out with the wood it
seems is and then and of course after we've built up the models in coffee we going to quantized them
and to quantized we're going to write a python script to quantized these loose so that we'll be able
to fit them into the memory of our microcontroller.
So we'll be performing quantized ation of our coffee models as well.
And then once that is done we'll see how to deploy our coffee models with the CMC as an end library.
And this is going to be written in C code.
So this is the um the roadmap.
If you've purchased the course and if you don't find any of these topics in this section Do not be.
Don't be bothered or do not worry at the end of the course when the course is complete.
All of this should be there.
So if you've purchased it now and you don't find topics relating to let's say coffee or terms of flow
then they are still in production.
This is the complete picture of the course and you can hold me to task on this right.
So yeah if you have any questions just let me know.
And this is the roadmap.
I'll see you in the next lesson.