Udacity – Intel® Edge AI for IoT Developers 2020-7 – Download

Description

Intel® Edge AI for IoT Developers is the name of the Intel® Edge artificial intelligence training course for IoT developers published by the prestigious Udacity Academy. What is Edge AI? What are the uses of this technology?

Edge computing runs processes locally on the device rather than in the cloud. This reduction in computing time allows for much faster data processing, eliminates the security risk of transferring data to a cloud-based server, and reduces the cost of data transfer as well as the risk of bandwidth outage. This disrupts performance. Computer vision and artificial intelligence are at the cutting edge of powering everything from factory assembly lines and retail inventory management to medical imaging equipment for hospital emergency care , such as X-ray and CT scanners. Drones, security cameras, robots, facial recognition from cell phones, autonomous vehicles, etc. all use this technology.

According to IEEE Innovation at Work, “by 2020, approximately 20 billion devices will likely be connected via the Internet of Things (IoT), generating incredible amounts of data every minute. The time taken to transfer data to cloud,services. It takes too long to come back to devices to meet the growing needs of the Internet of Things. Unlike cloud computing which relies on a single data center, edge computing works with a more distributed network and eliminates round trips. The cloud thus offers real-time responsiveness and local authority. Brings the most important traffic and processing closer to the application and end-user devices (smartphones, tablets, home security systems, etc.) that generate and consume data. “This significantly reduces latency. This leads to automated decision-making in real time. (IEEE)

70% of the data generated is at the edge, and only half of it is transferred to the public cloud. The rest will be stored and processed in Edge, which requires a different type of developer. The demand for professionals with edge artificial intelligence (AI) skills will be high as the edge artificial intelligence (AI) software market size is expected to grow from $355 million in 2018 to $1.15 billion in 2018. by 2023, with a CAGR of 27%. In Nanodegree Edge AI for IoT Developers, you harness the power of edge computing and use the Intel® distribution of OpenVINO™ Toolkit to accelerate the development of high-performance computer vision and deep learning inference applications .

What careers does this training prepare me for? This Nanodegree program prepares you for roles such as IoT Developer, IoT Engineer, Deep Learning Engineer, Machine Learning Engineer, Artificial Intelligence Specialist, VPU/CPU/FPGA Developer and more for businesses and organizations looking to innovate their hardware at the forefront. prepared

What you’ll learn in the Intel® Edge AI for IoT Developers course:

Use a pre-trained model for computer vision inference
Convert pre-trained models to framework-agnostic models
Perform efficient inference on deep learning models
Create an application on Edge, including sending information via MQTT and analyzing performance and usage pattern cases.
The importance of choosing the right material and the process involved in achieving it
Identify key specifications of Intel® processors and GPUs
Use Intel® Devcloud for the Edge to run deep learning on integrated CPUs and GPUs
Identify key Intel® VPU specifications
Intel® DevCloud for the Edge to run deep learning on VPU
Identify key features of Intel® FPGAs
And……

Course details:

Intel® Edge AI for IoT course content:

Deploy a people Counter application at the edge

Take advantage of the pre-Trained models
The model optimizer
The inference engine
Deploy an Edge application

Hardware for deploying computer vision and deep learning applications

Introduction to Hardware at the Edge
Integrated CPU and GPU
Vision processing units
Field-programmable door arrays

Optimization Techniques and Tools for Computer Vision Deep Learning Applications

Introduction to Software Optimization
Reducing model operations
Reduce model size
Other Software Optimization Techniques

Preconditions:

Intermediate knowledge of Python programming
Experience training and deploying deep learning models
Familiarity with different DL layers and architectures (CNN based)
Familiarity with the command line (bash terminal)
Experience with OpenCV

Pictures:

Intel® Edge AI for IoT Developers

Simple video:

installation guide ,

In order to view the course lessons in an organized and regular manner, run the index.html file and scroll through the videos through this file.