Deep Learning and Edge Computing for Satellite Systems

Deep Learning and Edge Computing
for Satellite Systems

The global remote sensing technologies market was valued at $13.1 billion in 2022 and is expected to reach $27.1 billion by 2028

Recording Available

5 Hours

Reserve seat for ₹399

Limited to 100 Students. Hurry Up!!!

LEARN ALL RELEVANT SKILLS

Spatial Analysis

K-Means Clustering

Edge Computing

CNN

Remote Sensing

Program Curriculum

Basics of deep learning
Edge computing
Hands on DL with Unet based architecture
Hands-on exercise
Project programming
Transfer Learning
Model Pruning

Understand the in depth concepts and tools you will learn throughout the program.

Explore our detailed curriculum!

Master essential AI tool

Gain hands-on experience with the tools used by professional remote sensing engineers to drive success and deliver value in the real world.

Key Learning

Basics of deep learning

Introduction to fundamental concepts and principles of deep learning.

Edge Computing

Exploration of computing processes on edge devices to enhance efficiency and reduce latency in AI applications.

Hands-on DL with U-Net Based Architecture

Practical experience in implementing deep learning models using U-Net architecture for image segmentation.

Transfer Learning

Techniques to leverage pre-trained models for new tasks to improve training efficiency and performance.

Model Pruning

Methods to optimize deep learning models by reducing their size and complexity without significantly affecting accuracy.

Mentored By

Arya Pratap

Employed as an edge computing scientist. Completed his bachelor’s degree in Acropolis, Indore. Secured AIR 4 in the entrance exam for IIRS. Impressed by his works, he got an invitation from NASA Johnson Space centre for his incredible work on moon. Awarded as the youngest scientist in ISRO at PRL. Presented multiple papers at NASA, JAXA, ESA and other space agencies. First Indian to perform edge computing. Published researches across multiple journals.