Deep Learning @ National Institute of Technology Andhra Pradesh

Participants Information

Participants:	40
Speakers:	07
Female participants:	15
Male participants:	25
Participants from SC/ST category:	06
Research Scholars:	02

Topics Covered

• Introduction to Machine Learning-I
• Machine Learning-II
• Semi-Supervised Learning/Transfer Learning
• Learning from Labeled Features and Weak Supervision(NF)
• Sequence Modeling(HMM/CRF)
• Recurrent Neural Networks & LSTM
• Sequence Labelling with CRF
• Tensorflow Usecases
• ML for NLP
• Author Profiling using Machine Learning
• Deep Representation for NLP
• Attention Modela for NI
• VAE for Computer Vision
• Evolution of CNN
• Generative Adversarial Networks
• Wasserstein GAN
• Self Organization Map(SOM) and greater self-organizing map(GSOM)
• Extreme learning machine(EML)
• Applications of Deep Machine Learning for anomaly detection
• Deep Models for Questioning and Answering
• Semi-Supervised Learning with GAN
• Cyclic GANs for Video Object Detection
• Image Captioning with Tensorflow

List of Speakers

Dr. Karthick Seshadri, Head, NIT Andhra Pradesh

Dr. K. Himabindu, Assistant Engineer, NIT Andhra Pradesh

Dr. Nagesh Bhattu, Assistant Professor, NIT Andhra Pradesh

Mr. N. Satya Krishna, Technical Director, IDRBT

Dr. Phani Krishna Karri, Adhoc Faculty, NIT Andhra Pradesh

Dr. Anand Kumar, Assistant Professor, NIT Surathkal

Dr. Avatharam Ganivada, Associate Professor, University of Hyderabad

Feedback Summary

All the topics were clearly explained.

Effective teaching and most valuable content.

Very good sessions and teaching methodology.

Suggestions from Participants

Effective hands on approach but required more hands on session.

Required more examples to learn the subject quickly.

More such FDP's are needed to learn Deep Learning perfectly.