Getting Started with Embedded AI @ (Hands on Experience)
.MP4 | Video: 1280x720, 30 fps(r) | Audio: AAC, 44100 Hz, 2ch | 1.48 GB
Duration: 3.5 hours | Genre: eLearning | Language: English
Build a realtime application to recognize fault of a DC motor by analyzing vibrational pattern via Embedded/EdgeAI(demo).

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.

Requirements

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

Description

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.

Who this course is for:

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



Download link:
Kod:
rapidgator_net:
https://rapidgator.net/file/4e4558407815dfb6fd39edb41fe3641a/52db5.Getting.Started.with.Embedded.AI..Hands.on.Experience.part1.rar.html
https://rapidgator.net/file/a0c546887309875bed73391ceedaba6d/52db5.Getting.Started.with.Embedded.AI..Hands.on.Experience.part2.rar.html

nitroflare_com:
https://nitroflare.com/view/61B6AB9D9B0461F/52db5.Getting.Started.with.Embedded.AI..Hands.on.Experience.part1.rar
https://nitroflare.com/view/E36A488B2BD0C5A/52db5.Getting.Started.with.Embedded.AI..Hands.on.Experience.part2.rar
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