Anthill Inside 2019

On infrastructure for AI and ML: from managing training data to data storage, cloud strategy and costs of developing ML models

Propose a session

End to End Computer Vision paradigm with respect to advanced deep learning techniques.

Submitted by Pushkar Pushp (@ppushp7) on Tuesday, 30 April 2019


Preview video

Technical level: Intermediate Section: Tutorials Session type: Tutorial

Abstract

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

Preview video

https://photos.app.goo.gl/2zWpZ26wQZGdEvGc7

Comments

  • Abhishek Balaji (@booleanbalaji) Reviewer 2 months ago (edited 2 months ago)

    Hello Pushkar/Shivani,

    Thank you for submitting a proposal. As per the policy of Anthill Inside, we only allow one presenter on stage per session. Please make a decision on who among you would be presenting this proposal, if selected.

    To proceed with evaluation, we need to see detailed slides and a preview video to supplement your proposal. Your slides must cover the following:

    • Problem statement/context, which the audience can relate to and understand. The problem statement has to be a problem (based on this context) that can be generalized for all.
    • What were the tools/options available in the market to solve this problem? How did you evaluate alternatives, and what metrics did you use for the evaluation?
    • Why did you pick the option that you did?
    • Explain how the situation was before the solution you picked/built and how it changed after implementing the solution you picked and built? Show before-after scenario comparisons & metrics.
    • What compromises/trade-offs did you have to make in this process?
    • What is the one takeaway that you want participants to go back with at the end of this talk? What is it that participants should learn/be cautious about when solving similar problems?
    • Is the tool free/open-source? If not, what can the audience takeaway from the talk?

    We need to see the updated slides on or before 21 May in order to close the decision on your proposal. If we do not receive an update by 21 May we’ll move the proposal for consideration at a future event.

  • Pushkar Pushp (@ppushp7) Proposer 2 months ago

    We will add the slides and demo video shortly.

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