Free Version in https://github.com/DLinIoTedge
Tricks and Guideline to Deploy AI in IoT (try) https://www.jkuse.com/home/jkevents/baranovichi ( FREE )
Introduction
Mathematical Theory
Data Set
AI Model Design
Training AI Model
Save AI Model
Load AI Model
Micro Inference Service
Deploy in Edge
Case Study 1: J7 App
Case Study 2: Kanshi
Case Study 3: IoT in 5G
Case Study 4: Healthcare
Case Study 5: Agriculture
Visual Recognition : IBM Watson
Silicon Vendor to Edge device
Recommended infra Architecture
FAQ
Authors
Niranjan Kumar had designed and implemented Back propagation algorithm island and other required code to train NN / CNN Deep Learning Models. Above mentioned contributions had come during his higher secondary school days. Presently, Niranjan Kumar is a 3rd year student of B.Sc. (Honours) Mathematics and Computer Science, Chennai Mathematical Institute. ( https://www.cmi.ac.in/people/student-profile.php?id=niranjan ) . Before joining CMI, Niranjan Kumar had worked extensively in Back Propagation Algorithm and its use in Optimization
Niranjan Kumar
Student - B.Sc. Mathematics
niranjan at cmi dot ac dot in
Jayakumar. S had worked on Dltrain platform by using contributions from Niranjan Kumar. DLtrain is used to train students in large scale by online and also as a tutorial session n IEEE conference during 2019. DLtrain is used to train faculties ( vai Faculty Development programs in many colleges and universittes ) in AI and deployment of AI in Edges.
Jayakumar S PhD IIT Mumbai
https://www.linkedin.com/in/jayakumarsingaram/
Baranovichi State University had used DLtrain to train their teaching staff and research students such that they can use it in AI projects. Same is given in details and also the same is used by many other students across the Global to learn deployment of AI applications in IoT edges.
