Cloud Computing

Workshop Target:

• Each module will have demos, case studies & practical implementations so that participants get the better understanding of the concepts.
• Our target goal will be to provide them enough information on the AWS services so that they can prepare themselves & gain certifications for AWS Cloud certified Practitioner and Solution Architect exams.

Research Topics:

• Research topics in cloud computing such as cloud resource provisioning, task scheduling, Green Cloud Computing, Edge and Fog computing will also be covered during the workshop.

Workshop Schedule:

Day 1
Introduction - Cloud Computing and Virtualization, Datacentre Cloud Service & Deployment Models, Mapping - Enterprise DC & AWS	Compute - EC2 (Elastic Compute Cloud) Storage - EBS (Elastic Block Store)
Network - VPC (Virtual Private Cloud) Network - ELB (Elastic Load Balancer)	Compute - Auto Scaling Network - Route53
Day 2
Storage - S3 (Simple Storage Service) Storage - EFS (Elastic File Store)	Security & Identity - IAM (Identity and Access Management) Command Line Interface
Database -RDS & DynamoDB Notification - Simple Notification Service (SNS)	Monitoring - CloudWatch Serverless Compute
Lambda Automation - Infrastructure as a Code (IaaC)	Cloud Trial Trusted Advisor

Lab Platform:

AWS Cloud (every participant will have to create own AWS free tier account to perform AWS Lab Hands-on)

Deep Learning

Deep Learning:

Workshop Target:

Each module will have demos, case studies & practical implementations so that participants get the better understanding of the concepts.

Our target goal will be to provide enough information on the Deep Learning, so the participant can utilize the gained knowledge for the development of various applications.

Workshop Schedule:

Day 1
Theory Session	Motivation: Foundations and Terminology of Deep Learning AI vs ML vs DL: A comparison Features and Weights Machine Learning Recap: Linear Regression, Logistic Regression Activation Functions Neural Networks Loss Functions Gradient Descent Feedforward and Backward Propagation Deep Learning Model training
Lab Session	Hands on with Python Building Neural Networks Lab
Theory Session	Dataset splitting and distribution Evaluation Metrics Error Analysis Bias vs Variance Performance comparison Regularization L1/L2 regularization, Dropout Early Stopping, Normalization Optimization methods Hyperparameter Tuning
Lab Session	Neural Network Hyperparameter Tuning and Optimization
Day 2
Theory Session	ANN vs CNN Convolution, pooling, padding, striding Inception Network Transfer Learning Applications of CNN Object Detection Landmark Detection Siamese Networks
Lab Session	Building Convolutional Neural Networks Analysis of convolution, pooling, padding and striding
Lab Session	ANN vs RNN Sequential Processing with RNN Forward and Back Propagation Language Models Sampling data from RNN
Lab Session	Building Recurrent Neural Networks Text Classification with Custom Dataset
Theory Session	LSTM and GRU Word Embedding Models Sentiment Analysis Image Captioning Machine Translation Speech Transcription
Online Exam

Advanced Deep Learning

Advanced Deep Learning:

Workshop Target:

Each module will have demos, case studies & practical implementations so that participants get the better understanding of the concepts.

Our target goal will be to provide enough information on the Advanced Deep Learning, so the participant can utilize the gained knowledge for the development of various applications.

Workshop Schedule:

Day 1
Hands on with Tensorflow 2.0 and PyTorch Unsupervised Deep Learning Sparse Coding Restricted Boltzman Machine
Day 2
Deep Belief Networks Autoencoders, Variational Autoencoders Generative Adveersial Networks (GANs) 3D CNN, YOLO v3, RefineDet, M2Det Image Segmentation, Style Transfer

Quantum Computing

Quantum Computing Workshop:

Workshop Target:

Each module will have demos, case studies & practical implementations so that participants get the better understanding of the concepts.

Our target goal will be to provide enough information on the Quantum Computing and Quantum machine Learning so the participant can utilize the gained knowledge for the development of various applications.

Workshop Schedule:

Day 1
Quantum System Physical phenomena Quantum Superposition principle Normalization Hermitian, Unitary Pauli matrices, Complex Vector Space, State Space: Hilbert Space
Day 2
Quantum states and Qubit, Representing Qubit States Quantum Algorithms Quantum Auto Encoder Quantum kernel estimation Quantum machine Learning

1. IoT and Drones
2. Robotic Process Automation
3. DevOps
4. Quantum Computing
5. Cybersecurity
6. Python
7. Image and Video Processing

Note: Workshop will be completely free of cost (online). In case of offline mode, Institution need to bear the cost of travel only for the two resource persons. Workshop will be conducted for a batch of maximum 50 students.

1. Advanced Deep Learning
2. BlockChain
3. Cloud Computing
4. Deep Learning
5. IoT and Drones
6. Robotic Process Automation
7. DevOps
8. Quantum Computing
9. Cybersecurity
10. Python
11. Image and Video Processing

Note: Workshop will be completely free of cost (online). In case of offline mode, Institution need to bear the cost of travel only for the two resource persons. Workshop will be conducted for a batch of maximum 50 students.