Deep Learning and Applications

Prospective Experts:

  • Industry support from NVidia, MathWorks (MATLAB)
  • Dr. Anupama Ray, IBM
  • Dr. Ritu, Intel
  • Prof. R. Venkatesh Babu, IISc Banglore
  • Prof. Aparajita Ojha, IIITDMJ
  • Dr. Santosh Vipparthi, MNITJ

Principal Coordinator :

  • Prof. Aparajita Ojha, IIITDM Jabalpur

9 – 13 Dec 2019

Download Brochure

Apply Online

Local Coordinator:
Dr. Irshad Ansari
Phone: 9109106995

Course contents:
Introduction to Machine Learning & Artificial Neural Networks: Overview of machine learning, Supervised and unsupervised learning , Artificial Neural Networks, Feedforward Neural networks, Gradient Descent and the back propagation algorithms, Regularization and Optimization. Difference between typical machine learning and deep learning Practice Session: Introduction to Python Programming, Tensorflow and Keras. Making a Neural Network, training and testing. Saving the best weights and model

Convolutional Neural Networks: Convolutional Neural Network (CNN), Convolution/Pooling layers, Activation maps, CNN as a feature extractor, Some Standard CNN architectures like AlexNet, VGGNet, GoogLeNet, ResNet and more recent networks Practice Session: Building a CNN model, CNN for image classification. Using Googel Colab for building and training Deep Learning Models.

Autoencoders and Generative Adversarial Networks: Autoencoders (AEs), Undercomplete and Overcomplete AE, Convolutional AE, Regularization, Sparsely regulated AEs, Denoising and Stacked AE. Generative Adversarial Networks (GAN), Variants of GAN. Practice Session: Using pretrained models, Transfer learning, Applying GoogleNet and ResNet for specific problems. Using Autoencoders

Recurrent Neural Networks: Brief Introduction to Recurrent Neural Networks, LSTM, GRU and their applications in machine translation, language modelling and sentiment classification.
Practice Session: Building an AI application for sentiment classification from travel\hotel website user feedback data.

CNN Application to Classification and Detection problems: Object detection algorithms, R-CNN, Faster R-CNN, YOLO and SSD. Hands on– Object detection. Practice Session: Installing Darknet framework on your laptop, how to use YOLO for object detection.

Registration Fee and Accommodation:
No Registration fee is charged for attending this programme planned at any designated academies/Remote centres. However, candidate should submit a Demand Draft/ CBS-Cheque of Rs.1000/- along with application form and the same will be handed over to the participant on the last day of the training. Certificate for participation as well as for Satisfactory performance will be given to the participants subject to fulfillment of attending all sessions, submission of assignments and clearing the test(s).

Boarding and Lodging at Hostels/Guest House will be provided at free of cost only at Identified E&ICT Academies. For details please refer to respective Academy websites. At identified Remote centres only working lunch and snacks will be provided. No Travel Allowance will be paid to the participants.

Core Team Members, E&ICT Academy:

Prof Aparajita Ojha

Prof. Vijay Kumar Gupta

Prof. P.N. Kondekar

Dr. Atul Gupta

Dr. Prashant Kumar Jain


Contact us :
Maj Neha Rawat (Retd) : +9893443284
Business Manager,
Electronics and ICT Academy
PDPM Indian Institute of Information Technology,
Design and Manufacturing, Jabalpur,
Dumna Airport Road, Jabalpur 482005