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8-9 Oct 2018: Training
9-11 Oct 2018: Tutorials & Conference
London, UK

Building a Pokédex to recognize Pokémon in real time using TensorFlow and object recognition

Anmol Jagetia (Media.net)
16:00–16:40 Wednesday, 10 October 2018
Implementing AI, Interacting with AI
Location: Westminster Suite
Secondary topics:  Computer Vision, Deep Learning tools

Who is this presentation for?

  • Engineers, data scientists, and those interested in using AI to solve real-world problems

Prerequisite knowledge

  • Familiarity with Python and TensorFlow
  • A basic understanding of object recognition (useful but not required)

What you'll learn

  • Explore an ML pipeline, object recognition techniques, and the TensorFlow Object Recognition API
  • Learn how to deploy ML models on a smartphone

Description

Back in the day, AI was a field dominated by people from academia or engineers from top companies. It required a very high level of mathematical understanding and very challenging technical training to be able to do anything with it. The recent popularization of AI, effective collaborative learning, the open-sourcing of bleeding-edge technology and tools, and the vast availability of high-quality data, along with cheap computing power, have opened the doors of innovation to the wider audience. Therefore, in recent years, its importance has greatly risen. TensorFlow enables us to reach that level of sophistication while leveraging technology and tools created by the very best, who have done a lot of heavy lifting for us, letting us just dive in and create something from the cool ideas.

Machine learning and object recognition have matured to the point that exciting applications are now possible. Anmol Jagetia demonstrates how to create a Pokédex that uses a camera phone to recognize the Pokémon it’s looking at in real time. You’ll explore core concepts of ML research, like gathering data, cleaning data, preparing a dataset, optimizing data to handle the problem, and then optimizing it to use as little computation as possible while still remaining robust enough to enable real-time predictions. You’ll learn how to reuse learned logic and models and use them over a set of similar problems and discover how to deploy your model to a mobile device, allowing a far more elegant solution to the problem than if it could just run it on a server.

Topics include:

  • An introduction to Pokémon and TensorFlow
  • A primer on basics of Python, the TensorFlow Object Recognition API, and object recognition techniques
  • An explanation of the ML pipeline
  • A short walkthrough of the entire process—how each step was done, the challenges faced, and the trade-offs made
  • Demo of an already trained model and a working implementation
Photo of Anmol Jagetia

Anmol Jagetia

Media.net

Anmol Jagetia is a software engineer at Media.net, one of the biggest ad tech companies globally, which was recently acquired by a Chinese consortium for $900M USD in the third-largest ad tech deals ever. Anmol is interested in machine learning, deep and reinforcement learning, web technologies, open source software, data science, and introducing people to technology. He has spoken at a number of conferences, including the O’Reilly AI Conference in New York in 2017. He has also authored some popular open source projects such as Flatabulous, which received over 2.2K stars on GitHub and has been downloaded close to a million times. Anmol was part of HPCC as a Google Summer of Code Student and interned at the prestigious Max Planck Institute for Software Systems, Germany, and Complutense University of Madrid, Spain. He graduated from the prestigious Indian Institute of Information Technology, Allahabad. He has also published his research with the IEEE and has forthcoming papers on interesting applied aspects of machine learning.

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Comments

Picture of Anmol Jagetia
Anmol Jagetia | SOFTWARE ENGINEER
4/11/2018 7:16 GMT

GitHub URL for all the code shown :
“Model”: https://github.com/anmoljagetia/oreilly-ai-pokemon
“Android”: https://github.com/anmoljagetia/oreilly-ai-pokemon-android
“ios”: https://github.com/anmoljagetia/oreilly-ai-pokemon-ios
“Webapp”: https://github.com/anmoljagetia/oreilly-ai-pokemon-webapp