Getting Started with Embedded AI | Edge AI

Name: Getting Started with Embedded AI | Edge AI
Price: 9 USD
Availability: InStock

3h 33m 42s

English

Paid

November 29, 2024

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

Requirements:

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

Who this course is for:

Embedded AI Explorer
Embedded Enthusiast
Engineers
Artificial Intelligence/Deep learning Enthusiast
M-Tech/PhD Students

What you'll learn:

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.

Watch Online Getting Started with Embedded AI | Edge AI

Join premium to watch

Go to premium

#	Title	Duration
1	What is an Artificial intelligence?	04:36
2	What is Machine Learning?	02:09
3	What is Deep Learning?	03:53
4	What is an Embedded/Edge AI?	04:53
5	Applications of Embedded AI	02:54
6	Overview of the Tools used.	01:47
7	What is Tensorflow?	06:11
8	What is Keras?	03:27
9	Comparison between Keras and Tensorflow	05:33
10	Installation of Keras and Tensorflow	01:22
11	What is STM32 and X-CUBE AI	01:55
12	Development Board used	01:14
13	What is Supervised Learning?	02:13
14	What is Unsupervised Learning?	01:59
15	Artificial Neuron Vs Real Neuron	02:19
16	What is an Artificial Neural Network?	02:36
17	What are layers and Forward propagation in NN	04:30
18	What is an Activation Function?	03:57
19	What is Gradient and Gradient Descent?	03:40
20	Optimization Algorithm and Loss function	04:24
21	How a Neural Network Learns?	04:27
22	The Concept of Loss functions in detail	02:56
23	The process of training and testing a NN	05:00
24	Why Overfitting occurs in NN and How to avoid it?	04:45
25	Why Underfitting occurs in NN and How to avoid it?	03:29
26	Hyperparameter of NN -> Learning Rate	03:16
27	What is Batch and Batch size of a Training samples?	03:19
28	Transfer Learning and Fine tuning Hyperparametrs in NN	05:21
29	What is Convolution?	06:06
30	What is a Convolution Layer in NN?	04:42
31	What is Max Pooling Layer?	03:58
32	What is Dropout layer?	01:44
33	One Hot Encoding of Output Classes or Labels	06:07
34	What is Confusion Matrix?	03:53
35	Difference between with or without normalization Confusion matrix	01:57
36	Introduction To Python and Writing first Program	06:25
37	Inroduction to Numpy Package	05:23
38	Introduction to Pandas Package	04:20
39	Introduction to Matplotlib	02:00
40	Key Steps for the implementation of Edge AI	03:27
41	Accelerometer Sensor Module	02:34
42	C code to capture data from Accelerometer	14:28
43	Python Script to Collect and Save Data in Binary file	08:52
44	Python script to Clean and Label Data	05:53
45	Defining a Convolution Neural Network to Learn from Captured Data	05:10
46	Python Script to Train the Neural Network	11:08
47	How we captured data and trained the model on it	02:10
48	Performance Evaluation of the Model (Plotting Confusion Matrix)	02:22
49	Convert KERAS model to c code	06:54
50	Integration of generated c code to acccelerometer module code	02:53
51	Infer the Fault State on the machine (demo)	03:11