schedule Aug 9th 10:45 - 11:30 AM place Grand Ball Room 2 people 146 Interested

At idealo.de we store and display millions of images. Our gallery contains pictures of all sorts. You’ll find there vacuum cleaners, bike helmets as well as hotel rooms. Working with huge volume of images brings some challenges: How to organize the galleries? What exactly is in there? Do we actually need all of it?

To tackle these problems you first need to label all the pictures. In 2018 our Data Science team completed four projects in the area of image classification. In 2019 there were many more to come. Therefore, we decided to automate this process by creating a software we called Image ATM (Automated Tagging Machine). With the help of transfer learning, Image ATM enables the user to train a Deep Learning model without knowledge or experience in the area of Machine Learning. All you need is data and spare couple of minutes!

In this talk we will discuss the state-of-art technologies available for image classification and present Image ATM in the context of these technologies. We will then give a crash course of our product where we will guide you through different ways of using it - in shell, on Jupyter Notebook and on the Cloud. We will also talk about our roadmap for Image ATM.

 
 

Outline/Structure of the Talk

  • Motivation why to use Image ATM
  • Introduction of image classification problem
    • Deep learning/Transfer learning
    • Keras, TensorFlow, PyTorch, MXNet etc can solve it but still low-level
  • Image ATM
    • Installation
    • CLI
    • Working with the cloud
    • Further roadmap
  • Conclusion

Learning Outcome

- Learn how to use Image ATM e.g. which kind of input is needed, preprocessing, training and then evaluation

- Learn how you can contribute to it as well

- Learn about our image classification problems

Target Audience

data scientists, machine learners, software engineers, data analyst

Prerequisites for Attendees

- Experience with deep learning in particular CNNs

- Experience with Jupyter notebooks & Cloud training

- Image classification

schedule Submitted 9 months ago

Public Feedback

comment Suggest improvements to the Speaker
  • Naresh Jain
    By Naresh Jain  ~  7 months ago
    reply Reply

    Hi Dat,

    Thanks for your proposal. Since the conference is targeted at Data Science practitioners, an overview session will not really be useful. Attendees are looking for specific deep-dive sessions, where they can learn something concrete, from the speaker's first-hand experience, which is hard to find online.

    Just a suggestion: Will you be able to take a specific use-case from online price comparison service and do deep into the nuts and bolts of it to help data science practitioners from other domain learn something unique and specific that you and your team implemented?

    • Dat Tran
      By Dat Tran  ~  7 months ago
      reply Reply

      It's Dat and not Dan. As I said I can present more a deep-dive into a specific use case instead of giving an overall overview. Shall I change the abstract here or should I hand in a different talk?

      • Naresh Jain
        By Naresh Jain  ~  7 months ago
        reply Reply

        I'm really sorry about misspelling your name, Dat. Request you to please update this proposal itself. Thank you.

        • Dat Tran
          By Dat Tran  ~  7 months ago
          reply Reply

          Done!

  • Dipanjan Sarkar
    By Dipanjan Sarkar  ~  7 months ago
    reply Reply

    Hey Dat, this is quite good but you folks have done so much recently around building excellent products\models leveraging ML and DL, would you maybe want to even cover some of those real-world case studies? 

    • Dat Tran
      By Dat Tran  ~  7 months ago
      reply Reply

      Sure I either can speak about all our use cases in general or I also have a new talk around our new project imageatm which we also used for a specific project. So depending on what you guys are interested in I can change the talk.


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    Automatically constructing and leveraging it in an intelligent system is an AI-hard problem, and an amalgamation of a wide variety of fields like natural language processing, information extraction and retrieval, graph algorithms, deep learning, etc.

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    To elucidate the differences between reasoning and pattern recognition: consider the problem of computer vision: the vision stack processes an image to detect shapes and patterns in order to identify objects - this is pattern recognition, whereas reasoning is much more complex - to associate detected objects with each other in order to meaningfully describe a scene. For this to be accomplished, a system needs to have a rich understanding of the entities within the scene and their relationships with each other.

    To understand a scene where a person is drinking a can of cola, a system needs to understand concepts like people, that they drink certain liquids via their mouths, liquids can be placed into metallic containers which can be held within a palm to be consumed, and the generational phenomenon that is cola, among others. A sophisticated vision system can then use this rich understanding to fetch details about cola in-order to alert the user of their calorie intake, or to update preferences for a customer. A Knowledge Graph's 'awareness' of the world phenomenons can thus be used to augment a vision system to facilitate such higher order semantic reasoning.

    In production systems though, reasoning may be cast into a pattern recognition problem by limiting the scope of the system for feasibility, but this may be insufficient as the complexity of the system scales or we try to solve general intelligence.

    Challenges in building a Knowledge Graph

    There are two primary challenges towards integrating knowledge graphs in systems: acquisition of knowledge and construction of the graph and effectively leveraging it with robust algorithms to solve reasoning tasks. Creation of the knowledge graph can vary widely depending on the breadth and complexity of the domain - from just manual curation to automatically constructing it by leveraging unstructured/semi-structured sources of knowledge, like books and Wikipedia.

    Many natural language processing tasks are precursors towards building knowledge graphs from unstructured text, like syntactic parsing, information extraction, entity linking, named entity recognition, relationship extraction, semantic parsing, semantic role labeling, entity disambiguation, etc. Open information extraction is an active area of research on extracting semantic triplets of object ('John'), predicate ('eats'), subject ('burger') from plain text, which are used to build the knowledge graph automatically.

    A very interesting approach to this problem is the extraction of frame semantics. Frame semantics relates linguistic semantics to encyclopedic knowledge and the basic idea is that the meaning of a word is linked to all essential knowledge that relates to it, for eg. to understand the word "sell", it's necessary to also know about commercial transactions, which involve a seller, buyer, goods, payment, and the relations between these, which can be represented in a knowledge graph.

    This workshop will focus on building such a knowledge graph from unstructured text.

    Learn good research practices like organizing code and modularizing output for productive data wrangling to improve algorithm performance.

    Knowledge Graph at Embibe

    We will showcase how Embibe's proprietary Knowledge Graph manifests and how it's leveraged across a multitude of projects in our Data Science Lab.

  • Liked Anant Jain
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    Anant Jain - Adversarial Attacks on Neural Networks

    Anant Jain
    Anant Jain
    Co-Founder
    Compose Labs, Inc.
    schedule 9 months ago
    Sold Out!
    20 Mins
    Talk
    Intermediate

    Since 2014, adversarial examples in Deep Neural Networks have come a long way. This talk aims to be a comprehensive introduction to adversarial attacks including various threat models (black box/white box), approaches to create adversarial examples and will include demos. The talk will dive deep into the intuition behind why adversarial examples exhibit the properties they do — in particular, transferability across models and training data, as well as high confidence of incorrect labels. Finally, we will go over various approaches to mitigate these attacks (Adversarial Training, Defensive Distillation, Gradient Masking, etc.) and discuss what seems to have worked best over the past year.

  • Liked Amit  Baldwa
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    Amit Baldwa - PREDICTING AND BEATING THE STOCK MARKET WITH MACHINE LEARNING AND TECHNICAL ANALYSIS

    Amit  Baldwa
    Amit Baldwa
    Director
    Finastra Financial Software
    schedule 7 months ago
    Sold Out!
    45 Mins
    Demonstration
    Intermediate

    Machine learning provides systems the ability to automatically learn and improve from experience without being explicitly programmed.

    Technical analysis shows in graphic form investor sentiment, both greed and fear. Technical analysis attempts to use past stock price and volume information to predict future price movements. Technical analysis of various indicators has been a time-tested strategy for seasoned traders and hedge funds, who have used these techniques to effective turn our profits in Securities Industry.

    Some researchers claim that stock prices conform to the theory of random walk, which is that the future path of the price of a stock is not more predictable than random numbers. However, Stock prices do not follow random walks.

    We will evaluate whether stock returns can be predicted based on historical information.

    Coupled with Machine Learning, we further try to decipher the correlation between the various indicators and identify the set of indicators which appropriately predict the value

  • Liked Pushker Ravindra
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    Pushker Ravindra - Data Science Best Practices for R and Python

    20 Mins
    Talk
    Intermediate

    How many times did you feel that you were not able to understand someone else’s code or sometimes not even your own? It’s mostly because of bad/no documentation and not following the best practices. Here I will be demonstrating some of the best practices in Data Science, for R and Python, the two most important programming languages in the world for Data Science, which would help in building sustainable data products.

    - Integrated Development Environment (RStudio, PyCharm)

    - Coding best practices (Google’s R Style Guide and Hadley’s Style Guide, PEP 8)

    - Linter (lintR, Pylint)

    - Documentation – Code (Roxygen2, reStructuredText), README/Instruction Manual (RMarkdown, Jupyter Notebook)

    - Unit testing (testthat, unittest)

    - Packaging

    - Version control (Git)

    These best practices reduce technical debt in long term significantly, foster more collaboration and promote building of more sustainable data products in any organization.