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School of AI

School of AI

Introductory meet-up

This section consists of the list of resources used in the introductory meet-up held on 22nd of September, 2018.


  • Jubeen Shah — Dean at school of AI in Raleigh
  • Brandon Johnson — Solutions Engineer at NetApp and CEO of Koanologies
  • David Ellison — Senior Artificial Intelligence Data scientist at Lenovo
  • Tao Wang — Sr. Manager at Artificial intelligence and machine learning R&D decision at SAS


  • List of presentation links will be updated after the meetup Here


  • Want to learn about markdown. Visit this site for information regarding formatting your Jupiter notebooks.

Code and demos

  • Neural net in 11 lines adding the link
  • Deep traffic
  • AI Flappy bird
    • Please note that you need to have anaconda installed on your PC for easy installation of dependencies.
      • It is perfectly fine if you don’t understand what is going on


  • Anaconda (Version >5.0, python version 3.x)
    • About
      • Anaconda is a distribution of packages built for data science.
      • It comes with conda, a package and environment manager.
      • You can use conda to create environments for isolating your projects that use different versions of Python and/or different packages.
    • Installation
    • Click here if you’re interested in the documentation
    • To understand how to use anaconda please visit this site
    • For environment creation and management please go through this link. Get a gist of the basics required for installation and management of environments.
      • If you prefer videos. This provides a very good summary for you to follow.
  • Jupyter Notebooks
    • This is a very interactive tool of learning and teaching python programming.
    • Good thing is, it comes preinstalled with anaconda. So you don’t have to install anything else.
    • Example
      • Want to learn about markdown. Visit this site for information regarding formatting your Jupiter notebooks.
        • There are some magic keywords for you to use in the notebooks. I’ll leave it for you to explore.

Additional reading before next meet-up

  • Read the part 1 of the deep learning book found here
    • It will give you a brief idea about the math and machine learning concepts to get started with deep learning and being comfortable with it.
  • You should use this cheat sheet for understanding any math notation.
  • If you’re uncomfortable with python programming I would recommend this course to get you started with python.
  • Interested in deep learning and machine learning? Sci-kit learn is your go to library
  • I recommend you to read up a bit on Perceptrons
    • These are the simplest forms of neural networks out there
  • Lets ascent the mountain of AI space with gradient descent... ¡wait what!
    • A process by which Machine Learning algorithms learn to improve themselves based on the accuracy of their predictions
  • Backpropogation
    • The process by which neural networks learn how to improve individual parameters.
  • Need a library for scientific computing? Numpy has you covered.
  • Tensorflow we’ll get there.

If nothing go through athese web pages. And make sure you bookmark them for easy access.



Siraj Raval

Jubeen Shah

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