Getting Started with Embedded AI | Edge AI

Getting Started with Embedded AI | Edge AI, available at $44.99, has an average rating of 3.54, with 51 lectures, based on 212 reviews, and has 1327 subscribers.

You will learn about 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. This course is ideal for individuals who are Embedded AI Explorer or Embedded Enthusiast or Engineers or Artificial Intelligence/Deep learning Enthusiast or M-Tech/PhD Students It is particularly useful for Embedded AI Explorer or Embedded Enthusiast or Engineers or Artificial Intelligence/Deep learning Enthusiast or M-Tech/PhD Students.

Enroll now: Getting Started with Embedded AI | Edge AI

Summary

Title: Getting Started with Embedded AI | Edge AI

Price: $44.99

Average Rating: 3.54

Number of Lectures: 51

Number of Published Lectures: 51

Number of Curriculum Items: 51

Number of Published Curriculum Objects: 51

Original Price: $69.99

Quality Status: approved

Status: Live

What You Will 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.

Who Should Attend

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

Target Audiences

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

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.

Course Curriculum

Chapter 1: Introduction to Embedded AI

Lecture 1: What is an Artificial intelligence?

Lecture 2: What is Machine Learning?

Lecture 3: What is Deep Learning?

Lecture 4: What is an Embedded/Edge AI?

Lecture 5: Applications of Embedded AI

Chapter 2: Tools Used and Installation

Lecture 1: Overview of the Tools used.

Lecture 2: What is Tensorflow?

Lecture 3: What is Keras?

Lecture 4: Comparison between Keras and Tensorflow

Lecture 5: Installation of Keras and Tensorflow

Lecture 6: What is STM32 and X-CUBE AI

Lecture 7: Development Board used

Chapter 3: Basic Concepts of AI and Deep Learning

Lecture 1: What is Supervised Learning?

Lecture 2: What is Unsupervised Learning?

Lecture 3: Artificial Neuron Vs Real Neuron

Lecture 4: What is an Artificial Neural Network?

Lecture 5: What are layers and Forward propagation in NN

Lecture 6: What is an Activation Function?

Lecture 7: What is Gradient and Gradient Descent?

Lecture 8: Optimization Algorithm and Loss function

Lecture 9: How a Neural Network Learns?

Lecture 10: The Concept of Loss functions in detail

Lecture 11: The process of training and testing a NN

Lecture 12: Why Overfitting occurs in NN and How to avoid it?

Lecture 13: Why Underfitting occurs in NN and How to avoid it?

Lecture 14: Hyperparameter of NN -> Learning Rate

Lecture 15: What is Batch and Batch size of a Training samples?

Lecture 16: Transfer Learning and Fine tuning Hyperparametrs in NN

Lecture 17: What is Convolution?

Lecture 18: What is a Convolution Layer in NN?

Lecture 19: What is Max Pooling Layer?

Lecture 20: What is Dropout layer?

Lecture 21: One Hot Encoding of Output Classes or Labels

Lecture 22: What is Confusion Matrix?

Lecture 23: Difference between with or without normalization Confusion matrix

Chapter 4: Introduction to Python and Python Packages Used

Lecture 1: Introduction To Python and Writing first Program

Lecture 2: Inroduction to Numpy Package

Lecture 3: Introduction to Pandas Package

Lecture 4: Introduction to Matplotlib

Chapter 5: Building Practical Application (Fault Recognition of a Motor on Edge)

Lecture 1: Key Steps for the implementation of Edge AI

Chapter 6: Data Capturing from Sensors (Practical)

Lecture 1: Accelerometer Sensor Module

Lecture 2: C code to capture data from Accelerometer

Lecture 3: Python Script to Collect and Save Data in Binary file

Chapter 7: Data Cleaning and Labeling (Practical)

Lecture 1: Python script to Clean and Label Data

Chapter 8: Building and Training of a Neural Network (Practical)

Lecture 1: Defining a Convolution Neural Network to Learn from Captured Data

Lecture 2: Python Script to Train the Neural Network

Lecture 3: How we captured data and trained the model on it

Lecture 4: Performance Evaluation of the Model (Plotting Confusion Matrix)

Chapter 9: Conversion of Model to C code (Practical)

Lecture 1: Convert KERAS model to c code

Lecture 2: Integration of generated c code to acccelerometer module code

Chapter 10: Infer the Result (Practical)

Lecture 1: Infer the Fault State on the machine (demo)

Instructors

Embedded Insider
Demysifying the Secrets of Embedded

Rating Distribution

1 stars: 15 votes
2 stars: 17 votes
3 stars: 41 votes
4 stars: 70 votes
5 stars: 69 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

Menu