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Getting Started with Embedded AI | Edge AI

Explained a demo application to recognize fault of a small DC motor by analyzing vibrational pattern via Embedded/EdgeAI
Instructor:
Embedded Insider
1.067 estudiantes matriculados
English [Auto]
Learn basic concept behind AI/DL
Learn how to use KERAS deep learning library in python?
Learn how to capture and label data from sensors via Microcontroller
Learn to create a Neural network and how to train them on data
Learn to implement Deep learning model on a microcontroller and can run inference on it.

Nowadays, you may have heard of many keywords like Embedded AI /Embedded ML /Edge AI, the meaning behind them is the same, I.e. To make an AI algorithm or model run on embedded devices. Due to a massive gap between both technologies, techies don’t know where to start with it.

So we thought to share our engineer’s experience with you via this course. We have created an application to recognize the fault of a motor based on the vibration pattern. An Edge AI node developed to perform the analysis on the data captured from the accelerometer sensor to recognize the fault.

             We have created detailed videos with animation to give our students an engaging experience while learning this stunning technology. We assure you will love this course after getting this hands-on experience.

The Motivation behind this course

                                                                 One and half years back, It was surprising when techies heard of the embedded systems running standalone Deep learning model. We thought to design an application using this concept and share the same with you via this platform.

How to start the course?

                                                               There are two possible ways to start this course. We have divided this course into Conceptual Learning and Practical Learning. You can either jump directly to the Practical videos to keep the motivation to learn and later can go to fundamental concepts. Or you can start with the basic concepts first then can start building the application.

What you will get after enrolling in the course

1. You will get Conceptual + Practical clarity on Embedded AI

2. After this course you will be able to build similar kind of applications in Embedded AI

3. You will get all the Python scripts and C code(stm32) for Data capturing ,Data Labeling and Inference.

4.You will be able to know in depth working behind the neural networks

  • Note – All the concepts are interlinked to each other may not possible to cover in one video. For more conceptual clarity keep on watching videos till the end. The doubt you will get in any video may get clear in another video. We tried to explain the same concept iteratively in different ways to make you familiar with the terminology.

  • If you have any question or doubt, at any point, please message us immediately. We are eagerly ready to help you out and will try to solve your doubt or problem asap.

Introduction to Embedded AI

1
What is an Artificial intelligence?
2
What is Machine Learning?
3
What is Deep Learning?
4
What is an Embedded/Edge AI?
5
Applications of Embedded AI

Tools Used and Installation

1
Overview of the Tools used.
2
What is Tensorflow?
3
What is Keras?
4
Comparison between Keras and Tensorflow
5
Installation of Keras and Tensorflow
6
What is STM32 and X-CUBE AI
7
Development Board used

Basic Concepts of AI and Deep Learning

1
What is Supervised Learning?
2
What is Unsupervised Learning?
3
Artificial Neuron Vs Real Neuron
4
What is an Artificial Neural Network?
5
What are layers and Forward propagation in NN
6
What is an Activation Function?
7
What is Gradient and Gradient Descent?
8
Optimization Algorithm and Loss function
9
How a Neural Network Learns?
10
The Concept of Loss functions in detail
11
The process of training and testing a NN
12
Why Overfitting occurs in NN and How to avoid it?
13
Why Underfitting occurs in NN and How to avoid it?
14
Hyperparameter of NN -> Learning Rate
15
What is Batch and Batch size of a Training samples?
16
Transfer Learning and Fine tuning Hyperparametrs in NN
17
What is Convolution?
18
What is a Convolution Layer in NN?
19
What is Max Pooling Layer?
20
What is Dropout layer?
21
One Hot Encoding of Output Classes or Labels
22
What is Confusion Matrix?
23
Difference between with or without normalization Confusion matrix

Introduction to Python and Python Packages Used

1
Introduction To Python and Writing first Program
2
Inroduction to Numpy Package
3
Introduction to Pandas Package
4
Introduction to Matplotlib

Building Practical Application (Fault Recognition of a Motor on Edge)

1
Key Steps for the implementation of Edge AI

Data Capturing from Sensors (Practical)

1
Accelerometer Sensor Module
2
C code to capture data from Accelerometer
3
Python Script to Collect and Save Data in Binary file

Data Cleaning and Labeling (Practical)

1
Python script to Clean and Label Data

Building and Training of a Neural Network (Practical)

1
Defining a Convolution Neural Network to Learn from Captured Data
2
Python Script to Train the Neural Network
3
How we captured data and trained the model on it
4
Performance Evaluation of the Model (Plotting Confusion Matrix)

Conversion of Model to C code (Practical)

1
Convert KERAS model to c code
2
Integration of generated c code to acccelerometer module code

Infer the Result (Practical)

1
Infer the Fault State on the machine (demo)
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2 estrellas
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Garantía de devolución de dinero de 30 días

Incluye

4 horas de video a pedido
Acceso completo de por vida
Acceso en el móvil y en la televisión
Certificado de finalización
Getting Started with Embedded AI | Edge AI
Precio:
$79.99 $15
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