Good day.
This is from the educational engineering tech.
Today we'll be discussing on daily email with the new terminal.
Forward to this presentation outline on Tiny Hamlet New Terminal.
This is actually comprised with convalescence.
And on this lessons we will be tackling on five projects.
First, on the lesson, one will be giving you a short introduction on what four lesson one on introduction
to tiny email with real terminal will be giving you a short background and discussion for what we're
going to go through throughout this presentation.
So what is machine learning?
It says here that it is a branch of artificial intelligence focused on building applications that learn
from data and improve their accuracy over time without being programmed to do so.
The foundation of machine learning is that rather than have to be thought to do everything step by step
machines, if they can be programmed to think like us, can learn to work by observing, classifying
and learning from its mistakes, just like we do at deep learning is a subset of machine learning that
utilizes artificial neural networks for learning from large amounts of data.
So what it means is that an artificial neural network is an attempt to stimulate the network of neurons
that make up a human brain.
Now, the ancient art created by programming computers to behave as though they are interconnected brain
cells in order for Ian and still learning.
They have to have a tremendous amount of information thrown at them called a train set.
When you're trying to teach an alien and how to differentiate, it cut from the dog.
The training set will provide thousands of images tagged as a dog so that the network would begin to
learn.
Once it has been trained with significant amount of data, it will try to classify future database on
what it thinks it's seeing or hearing, depending on the dataset throughout the different units.
Now, during the training period, the machine's output is compared to the human provided description
of what should be observed if there is the scene.
The machine is validated if it's incorrect.
It uses back propagation to adjust its learning, going back through the layers to tweak the mathematical
equation known as deep learning.
Now this is what it makes as a network intelligence.
Normally, deep neural networks require rather powerful computing resources to be trained and deployed.
However, recently a batch of Mel on the edge or embedded machine learning called Dynamic have appeared.
It represents a technique or field of study in machine learning and embedded systems that explores which
machine learning applications once reduced, optimized and integrated, can be run on devices as small
as microcontrollers.
Now what is the relevance of titanium and its importance?
It says here that the tiny arm in the arm, as you might have guessed it, stands for machine learning.
And in most cases nowadays it refers to deep learning.
Tiny in dynamical means that the models are optimized to run on very low power and small footprint devices
such as various MSU's.
Then, this year that embedded devices come in all sorts of shapes and sizes, starting from embedded
supercomputers such as Nvidia Jetson Exchanger SGX to the tiniest of microcontrollers, for example
the ESB 32 or Cortex M0.
Now why?
Embedded on microcontrollers is put in a special category and even given its own cool name.
Now this is because it comes with its own set of advantages and limitations.
The attractions of titanium is in fact the misuse use are ubiquitous small consume, small amounts of
energy and comparatively cheap.
They are M Cortex, M0 Plus and the little seed.
We know shuffleboard, which is built around it.
The word easy, small as thumb.
It consumes only one point thirty three mph of power, which means it can work for more than a hundred
and twelve hours on a 150 m battery, much more if put in deep sleep, and it only costs as little as
4.3 US dollars.
Now, improved model optimizations and frameworks for running machine learning model inference on microcontrollers
have actually made it possible to add more intelligent.
These small devices making them more useful and smarter neural networks can now be used as microcontrollers
such as bulges, scene recognition, for example, elephant activity or a sound of breaking glass.
We also have hotword detection routine to activate the device with a specific phrase or even for simple
image recognition tasks.
They can be used to give a new life to old sensors, like using an accelerometer on a machine to look
for problems and make sure it doesn't break down before it does.
Or they can be used to tell different kinds of liquors apart.
Like in this demo, Tiny Animal has a lot of different ways you can use it.
Now, you might want to ask what about its limitation now on the main limiting factor is actually the
rum or the size of the MCU.
No matter how you will optimize it, you wouldn't be able to fit that YOLO 999 into a tiny microcontroller.
Same goes as for automatic speech recognition.
Well, simple work or voice command detection is possible.
Open domain speech recognition is out of reach of use for now.
Now what are the preparations we need to make now in this course will mainly be using the ear and cortex
and for gore.
Inside we use Terminal Development Board.
W Terminal is a perfect tool to get started with the I o d and dynamically.
Now it is built around the HTC M d 51 P19 chip with a R M Cortex M4 f gore running to 120 hertz, which
is very well supported by various frameworks for Ml interference on microcontrollers.
Now it's just you that this board also has built in light sensor microphone, programmable buttons,
2.4 inch LCD display, accelerometer to groove ports for easy connection of more than 300 various global
ecosystem sensors in terms of software.
We'll be using the Arduino IDE to write software that will run on devices as well as Edge Impulse and
TensorFlow Lite for microcontrollers to train and test models.
It's very easy to use edge impulse as a development platform for machine learning on edge devices.
It has a web interface and toolkit for dual dynamic pipeline, from collecting data to deploying models
needed for our next letter.
I will be showing you how you can use your TensorFlow Lite for microcontrollers to build your own model
training and inference pipeline.
Now installing Arduino, I'd make sure you get the latest version from the download page.
You can choose between installer and the zip packages.
We suggest you use the first one that installs directly everything you need to use Arduino software,
including the drivers.
And then when the download finishes, make sure to proceed with installation and please allow the driver
installation process.
When you get a warning from the operating system.
So before we set on our journey to explore the possibilities of tiny email, we need to set up the working
environment, namely the Arduino IDE Edge Impulsively and TensorFlow.
So here are the next steps in installing Arduino, I'd made sure to choose the components to install,
as shown in the illustration.
Then right after that, just the installation directory.
And we suggest that to keep the default one, the next to compile and upload code for wheel terminal,
you will need to install wheel terminal specific tools in Arduino IDF.
And the first step is add additional boards manager.
You are else.
Make sure to open your original ID, click on the file, then proceed to preferences and then copy below
the you are l to additional board manager if you are else, and then step two install the wheel terminal
tools.
Click on tools and then after that, go to boards.
And then right after clicking boards, proceed two boards manager and search their wheeled terminal
in the board's manager.
Then if you find it.
Press on install and wait until the installation process finishes.
And then lastly, select your board and port.
You'll need to select the entry in the tools and then click on board menu that corresponds to your Arduino.
Select the real terminal.
For installing Edge Impossible II control local devices with this Edge Impossible II that would act
as a proxy for devices that don't have internet connection and upload and convict local files with the
CNI, which is called the edge impossible.
Eight.
So basically first install node the jazz version 10 or higher on your host computer.
Now this is applicable for Windows users installed the additional Node.js tools when prompted.
You may skip this setup if you already have Visual Studio 2015 or more.
Then after that, install these Yeelight tools via the npm install edge impulse like a words you should
have the tools available in your path.
This is just optional in installing TensorFlow for this part.
We will be using Python and TensorFlow for more in-depth lessons later in the course.
Then look, the installation of TensorFlow is, like I said, it is optional, though if you can get
access to Google collab an only environment where you can execute Python code and trainer modules.
So to install TensorFlow on Windows first install Mini Conda, which is the Python, the Virtual Environment
Manager, which you can download from the official website and then create a new virtual environment.
With this code conduct eight slash and Python equals 3.8.
Then third, install TensorFlow in deep in the environment.
Install TensorFlow.
Then lastly, next time you need to use TensorFlow, simply open Winik on the prompt and activate the
virtual environment you created with the code to activate Malcolm.
So make sure to explore ready to use demos of Edge Impulse and TensorFlow by uploading sample codes.
First is that magic wand with TensorFlow.
Second is that voice command detection with E-ELT.