Understanding and Implementing Auto-Encoders Using Python
Considering the fact that businesses these days make a lot of money by recommending customers the things that match their likes, knowing how to build a Recommendation System would be of great use to many aspiring Deep Learning enthusiasts. This workshop is all about understanding and implementing Auto-Encoders.
Auto-Encoders are the Unsupervised Deep Learning Models which are widely used for Dimensionality Reduction and Feature Discovery. New types of Auto-Encoders have enabled us to build very nice Recommendation Systems. The talk will focus on understanding Auto-Encoders, their types, and building a Recommender System that Predicts Rating (1 - 5) using PyTorch.
The flow of the workshop will be as follows:
- Self Introduction
- Introduction to Unsupervised Deep Learning
- Diving DEEP into Auto-Encoders (Theory, Architecture, and Working)
- Introduction to Sparse Auto-Encoders
- Introduction to Denoising Auto-Encoders
- Introduction to Contractive Auto-Encoders
- Introduction to Stacked Auto-Encoders
- Understanding the Deep Auto-Encoders
- Training Auto-Encoders
- Building a Recommender System that Predicts Ratings (1 - 5)
- Understanding the Problem of Overcomplete Hidden Layers
- End of talk
- Questions and Answers Session
- Familiarity with programming in Python.
- Basic knowledge of Linear Algebra, Probability Theory, and Statistics.
- A basic idea of how Artificial Neural Networks work.
- Some experience with Keras, TensorFlow, or PyTorch will be good but not necessary.
- 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.
- 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.
- Recently talked about "Understanding and Implementing Recurrent Neural Networks using Python" at GeoPython, Basel, Switzerland'18.
- Will be speaking about Artificial Neural Networks at EuroPython 2018, Edinburgh, Scotland.
- A Microsoft Certified Professional, Microsoft Technology Associate, IBM Certified Web Developer, and Hewlett Packard Certified Developer.
- Has 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).
My compact Biography: My name is Anmol Krishan Sachdeva. I am currently pursuing MSc Advanced Computing from University of Bristol, United Kingdom. My specialization is in AI, ML, Applied Data Science, Computer Vision, and Computational Neuroscience. I am also doing research work on Neural Networks and Computational Neuroscience and hence understand the nitty-gritty of the subject. Deep Learning is a Black Art and I want to impart knowledge of this Black Art to people who are willing to learn. This conference is the right place to deliver the knowledge. Looking forward to speaking at the conference.