Anthill Inside 2019

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

Outline/Structure of the Tutorial
What is CNN ?
Classic Architecture
Resnet
Inception
Others
Data Augmentation
Transfer Learning
Fine Tuning
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.

Requirements

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.

Speaker bio

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.

Co-Author
Shivani Naik
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.

Slides

https://www.slideshare.net/secret/BKaoqAEZx0glH3