Mô tả

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.

Bạn sẽ học được gì

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.

Yêu cầu

  • Knowledge of C or Python Language is plus
  • Knowledge of stm32 is plus

Nội dung khoá học

10 sections

Introduction to Embedded AI

5 lectures
What is an Artificial intelligence?
04:35
What is Machine Learning?
02:08
What is Deep Learning?
03:52
What is an Embedded/Edge AI?
04:52
Applications of Embedded AI
02:53

Tools Used and Installation

7 lectures
Overview of the Tools used.
01:46
What is Tensorflow?
06:10
What is Keras?
03:26
Comparison between Keras and Tensorflow
05:32
Installation of Keras and Tensorflow
01:21
What is STM32 and X-CUBE AI
01:54
Development Board used
01:13

Basic Concepts of AI and Deep Learning

23 lectures
What is Supervised Learning?
02:12
What is Unsupervised Learning?
01:58
Artificial Neuron Vs Real Neuron
02:18
What is an Artificial Neural Network?
02:35
What are layers and Forward propagation in NN
04:29
What is an Activation Function?
03:56
What is Gradient and Gradient Descent?
03:39
Optimization Algorithm and Loss function
04:23
How a Neural Network Learns?
04:26
The Concept of Loss functions in detail
02:55
The process of training and testing a NN
04:59
Why Overfitting occurs in NN and How to avoid it?
04:44
Why Underfitting occurs in NN and How to avoid it?
03:28
Hyperparameter of NN -> Learning Rate
03:15
What is Batch and Batch size of a Training samples?
03:18
Transfer Learning and Fine tuning Hyperparametrs in NN
05:20
What is Convolution?
06:05
What is a Convolution Layer in NN?
04:41
What is Max Pooling Layer?
03:57
What is Dropout layer?
01:43
One Hot Encoding of Output Classes or Labels
06:06
What is Confusion Matrix?
03:52
Difference between with or without normalization Confusion matrix
01:56

Introduction to Python and Python Packages Used

4 lectures
Introduction To Python and Writing first Program
06:24
Inroduction to Numpy Package
05:22
Introduction to Pandas Package
04:19
Introduction to Matplotlib
01:59

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

1 lectures
Key Steps for the implementation of Edge AI
03:26

Data Capturing from Sensors (Practical)

3 lectures
Accelerometer Sensor Module
02:33
C code to capture data from Accelerometer
14:27
Python Script to Collect and Save Data in Binary file
08:51

Data Cleaning and Labeling (Practical)

1 lectures
Python script to Clean and Label Data
05:52

Building and Training of a Neural Network (Practical)

4 lectures
Defining a Convolution Neural Network to Learn from Captured Data
05:09
Python Script to Train the Neural Network
11:07
How we captured data and trained the model on it
02:09
Performance Evaluation of the Model (Plotting Confusion Matrix)
02:21

Conversion of Model to C code (Practical)

2 lectures
Convert KERAS model to c code
06:53
Integration of generated c code to acccelerometer module code
02:52

Infer the Result (Practical)

1 lectures
Infer the Fault State on the machine (demo)
03:10

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