Brief Description:


“One of the most interesting features of machine learning is that it is on the boundary of several different academic disciplines, principally computer science, statistics, mathematics, and engineering. Machine learning is usually studied as part of artificial intelligence, which puts it firmly into computer science. Understanding why these algorithms work requires a certain amount of statistical and mathematical sophistication that is often missing from computer science undergraduates.” Appears that the Convolutional Neural Network is a very new and yet proven tool to model a given physical process as long as, given physical process can be captured in the form of Images or in the forming of Video.


Business owners for enterprises of all sizes are struggling to find the next generation of solutions that will unlock the hidden patterns and value from their data. Many organizations are turning to artificial intelligence (AI), machine learning (ML) and deep learning (DL) to provide higher levels of value and increased accuracy from a broader range of data than ever before. They are looking to AI to provide the basis for the next generation of transformative business applications that span hundreds of use cases across a variety of Industry verticals. AI, ML and DL have become hot topics with global IT clients. They are driven by the confluence of next-generation ML and DL algorithms, new accelerated hardware and more efficient tools to store, process and extract value from vast and diverse data sources that ensure high levels of AI accuracy. However, AI client initiatives are complex and often require specialized skills, ability, hardware and software that is often not readily available. AI enabled application deployment includes both the software and the hardware infrastructure that are deeply optimized for a complete production AI system.


Objectives and Scope:

Objective: Provide working knowledge to handle “ get ready with tool set to develop DL applications and deploy Sample DL application in IoT Edge”

Recommended for DL in Cloud Native and Edge Native

( 30 hours )

1a , 1b ( Data Set Handling )

2a, 2b, 2c,2d ,2e , 3, 4 ( Training DL model )

6a, 6b, 6c, 6d ( Inference as a Micro service )

5a, 5b, 5c, 5d ( Inference in on prime CPU , iot Edge )

7b ( Create your own custom Data Set )

7a, 7b, 7c, 7d , Explore theory side of DL Model )

Recommended for DL in Edge Native ( 10 hours )

7d, 5d, 5a, 5b and 7b ( Create your own Data Set )

Recommended for DL in Cloud Native (6 hours )

1b ( Data Set Handling )

2b ( Training DL model )

6d ( Inference as a Micro service )

7c, 7b ( Create your own Data Set )