Understanding and Implementing Generative Adversarial Networks (GANs)

One of the BIGGEST Breakthroughs in the Deep Learning Revolution

Anmol Sachdeva

Computer Vision Data Science Deep Learning Machine-Learning Python 3

The advancements in the field of Deep Learning are approaching a breakneck speed. Recent years have witnessed enormous research activities in Deep Learning, and Generative Adversarial Networks (GANs) is one of them. GANs are one of the most intriguing Deep Nets that have ever been built. GANs belong to a class of algorithms called the Generative Algorithms which help in predicting features given a certain label. This has led to the generation of artificial content (like images, music, speech, prose, and much more). Generative Adversarial Networks have a wide array of applications in the real world (including Super-resolution imaging). This talk aims at discussing the working of GANs, their applications to the real world (including Geo-Imagery), and demonstrating a quick hands-on code implementation using Python.

The flow of the talk will be as follows:
~ Self Introduction
~ A Succinct Prelude to Deep Learning
~ Understanding Discriminative and Generative Algorithms
~ A Brief Introduction to Adversarial Networks
~ Working and Architecture of Generative Adversarial Networks (GANs)
~ Quick Hands-on and Code Walkthrough (using Python)
~ Tips to Train GANs Better
~ Getting to know one of the Strongest Counterparts of GANs: Variational Autoencoders
~ Discussing the Applications of GANs
~ Roadmap to Further Study About GANs
~ End of Talk
~ Questions and Answers Session

Type: Talk (45 mins); Python level: Intermediate; Domain level: Intermediate

Anmol Sachdeva

Bigbasket

Speaking at PyCon Thailand and GeoPython this year.
Last year, I delivered talks at EuroPython and GeoPython and the response was overwhelming. It was an honour to be part of such amazing events and communities. The connections that I made last year seems to be like life long contacts. I share a deep bond with the PyCon Community and hope for much more learning, exchange of ideas, excitement, and fun this year.

Compact Biography:
~ Currently, working as a Platform Software Engineer at Bigbasket, India (India's largest online food and grocery store).
MSc in Advanced Computing (Machine Learning, Artifical Intelligence, Robotics, Cloud Computing, and Computational Neuroscience), University of Bristol, United Kingdom.
~ International Tech Speaker (spoke at numerous National and International Conferences).
~ Last year, gave a talk about "Recurrent Neural Networks and Long Short-Term Memory Networks (LSTMs)" at EuroPython, Edinburgh, Scotland - July 2018. Link: [Recurrent Neural Networks and Long Short-Term Memory Networks](https://ep2018.europython.eu/conference/talks/understanding-and-implementing-recurrent-neural-networks-using-python)
~ Last year, gave a talk about "Understanding and Implementing Recurrent Neural Networks using Python" at GeoPython, Basel, Switzerland - May 18. Link: [Understanding and Implementing Recurrent Neural Networks using Python](http://2018.geopython.net/#s107)
~ Have 8+ International Publications. [Latest work got published in ACM CHI 2018. The project was exhibited in Montreal, Canada.]
~ Received 6 Honours and Awards (International and National level).
~ Represented India at International Hackathons like Hack Junction’16, Finland and Hack the North’16, Canada. Got invited for more than a ‘dozen’ of prestigious International Hackathons (PennApps’17, HackNY’17, Hack Princeton’17 and many more) and Conferences.
~ A Microsoft Certified Professional, Microsoft Technology Associate, IBM Certified Web Developer, and Hewlett Packard Certified Developer.
~ Former Software Developer Intern at IBM & an ALL STACK DEVELOPER capable of designing and developing solutions for Mobile, Web, Embedded Systems, and Desktop. Areas of interest are Computational Neuroscience, Deep Learning, and Cloud Computing.