##About the 2019 edition:
The schedule for the 2019 edition is published here: https://hasgeek.com/anthillinside/2019/schedule
The conference has three tracks:
- Talks in the main conference hall track
- Poster sessions featuring novel ideas and projects in the poster session track
- Birds of Feather (BOF) sessions for practitioners who want to use the Anthill Inside forum to discuss:
- Myths and realities of labelling datasets for Deep Learning.
- Practical experience with using Knowledge Graphs for different use cases.
- Interpretability and its application in different contexts; challenges with GDPR and intepreting datasets.
- Pros and cons of using custom and open source tooling for AI/DL/ML.
#Who should attend Anthill Inside:
Anthill Inside is a platform for:
- Data scientists
- AI, DL and ML engineers
- Cloud providers
- Companies which make tooling for AI, ML and Deep Learning
- Companies working with NLP and Computer Vision who want to share their work and learnings with the community
For inquiries about tickets and sponsorships, call Anthill Inside on 7676332020 or write to firstname.lastname@example.org
Sponsorship slots for Anthill Inside 2019 are open. Click here to view the sponsorship deck.
End to End Computer Vision paradigm with respect to advanced deep learning techniques.
Deep learning based approaches to solve image classification have become a core technology in AI, largely due to developments in computing powers and digital data. However image classification gained popularity beyond academic circle with the advent of visual object recognition challenge.
In this talk, we will walk through the journey of deep learning in the field of computer vision. The main focus will be on the most recent and advanced technique for image classification and object detection .We will walk through various classical architecture and in the journey will learn concepts like padding, max pooling .
To make the talk more interactive we will show live demo and code run of various use cases like car detection for autonomous driving.
Keywords: Object detection, Transfer Learning, Art transfer, Max-Pooling,Padding
Outline/Structure of the Tutorial
What is CNN ?
Case Study and applications
Autonomous Driving Car Detection
Overview of transfer learning
We will explain implementation of case study with jupyter notebook to get hands on experience.
Basic understanding of deep learning and how neural networks are trained. Beginner level knowledge about Python and Keras will be helpful in understanding the concepts more efficiently.
Pushkar Pushp is working as a Data Scientists with WalmartLabs having done his graduation and masters in statistics from ISI, Kolkata. His areas of interests range from pure Mathematics, Python to Computer Vision, Deep Learning. He has extensively work on Keras/tensorflow to develop various state of art models such as Face Recognition,Trigger Word detection ,Machine Translation and other sequence models.
I have a Master’s degree in Information Technology with a Data Science major from IIIT Bangalore. Currently, I am working on Computer Vision as a Statistical Analyst at Walmart Labs India. With projects that make use of different ML techniques like object detection, GANs, CNNs, recommendation systems, I have worked with Machine Learning for the past 4 years. I also have a provisionally filed patent titled ‘System and method for produce detection and classification’ for an image classification algorithm.