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DIY Applied Machine Learning

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Presented at IDEAS Conference Dallas at the University of Texas in Dallas 2018. Subtitle: Practical and collaborative method to jump start into machine learning projects using open source with Jupyter Notebooks and Google Collab. Abstract: Not at all machine learning enthusiasts are alike, and, hence, setting up code environment or training the data model can many times be overwhelming for newcomers in the field of machine learning and deep learning. Data scientists may not have the necessary skills in setting up development environments, and, programmers, may not necessarily have the data scientists skills for preparing data sets and evaluating machine learning models. Furthermore, data scientists and programmers together in some enterprise may lack the collaboration platforms to work together on such projects. Even though most of the books on machine learning using some programming language X provide readers with instructions for setting up the coding environment, such chapters can derail the process of getting started if one gets stuck in the setup. Alternatively, collaborative platforms using Jupyter Ipython Notebooks and Google Collab provide a quick starter for programmers and data scientists alike to develop and collaborate on machine learning algorithms through open source without getting stuck with the nuances of setting up their environments or having to depend on commercial products. The talk revisits the value of Jupyter notebooks for newcomers to the field of AI by showcasing live examples and sharing sources of machine learning algorithms running online using Jupyter notebooks and providing a set of guidelines for implementing such technology in the workplace or leveraging existing ones in the market such as Google Collab. The talk is intended to encourage machine learning enthusiasts to enter the field through a practical method that minimizes the stress from the overwhelming material available on the Internet on how to get started with machine learning.

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DIY Applied Machine Learning

  1. 1. DIY Applied Machine Learning: Practical and Collaborative Method to Jump Start into Machine Learning with Jupyter Notebooks and Google Collab Tarek Hoteit - PhD, Director of IT @ Thomson Reuters http://tarek.computer IDEAS Conference Dallas 2018
  2. 2. About me - Tarek Hoteit First encounter with a “computer” was at 6 years of age with Atari Pacman Still fascinated with 8-bit old-school programming & retro games 34 years later My first lines of code when I was 8 years old: 10 Print “hello” 20 goto 10 Got obsessed with text adventure games which lead to natural language processing and terminal- based coding Once in 80s encountered an article on neural networks for Commodore 64 that made no sense to me .. but that curiosity stayed deep in my brain ... Continue to be passionate about computers: coding, tinkering with IoT devices, playing retro games, running servers...
  3. 3. Curiosity drove me to love computers and got me into artificial intelligence, machine learning, etc. How about you? How do you get into AI by yourself? https://image.slidesharecdn.com/100608pkn-100608043211-phpapp01/95/curiosity- empathy-and-imagination-6-728.jpg?cb=1309980347
  4. 4. Traditionally you go .... on Twitter - FOLLOW Machine Learning experts on Twitter - READ tweets about new implementations
  5. 5. Or..... check LinkedIn ... - Follow LinkedIn profiles and or posts
  6. 6. Then .... you go to YouTube - Watch tutorials - Read comments
  7. 7. Then you decide to take a course online - Pick up a course on Coursera - Follow the tutorial - Use predefined environments for your projects
  8. 8. Then what?
  9. 9. You ask yourself.... - How do I start on my own? - What project can I do? - How can I show my work? - How do I setup the environment for myself? - What is a great use case to build on? - Which framework would I use? - Which language would I choose? - Will I get employed with my knowledge? - Others know more than me? - And on and on and on...
  10. 10. It gets more difficult to decide ...
  11. 11. Yes.... machine learning ... and deep learning ... and ..... and.... can be overwhelming.. but........
  12. 12. ““Everything is hard before it is easy” - Goethe
  13. 13. And the way to do it yourself with machine learning? 4 STEPS
  14. 14. DO IT YOURSELF with machine learning - step #1: theory ● Understand what is supervised, unsupervised, deep learning, and reinforcement learning concepts ○ Check mindmap summarizing concepts from data analysis to deep learning here ○ Business people can read this, coders can read that, and scientists can read these arxiv.org
  15. 15. DO IT YOURSELF with machine learning - step #2: code ● Start with a blank but preset Jupyter notebook - use Google Collab https://colab.research.google.com or Microsoft Azure Notebooks https://notebooks.azure.com/ ● Learn some critical basics using Jupyter notebook as a playground - check https://jupyter- notebook.readthedocs.io/en/stable/ ● Basic Python learning - don’t go deep.. Just how to start writing basic lines of code - check https://www.python.org/about/gettingstarted/ ● Delimited files concept / CSV how to import and how to export - love Pandas! - check https://pandas.pydata.org/pandas-docs/stable/io.html ● Package installation - PIP & setting up requirements.txt for storing all packages to install - check https://packaging.python.org/tutorials/installing-packages/ ● Understand and use Python virtual environments - check http://docs.python- guide.org/en/latest/dev/virtualenvs ● Learn markdown syntax for content publishing https://github.com/adam-p/markdown- here/wiki/Markdown-Cheatsheet
  16. 16. https://colab.research.google.com Interactive scripting & ability to install packages directly Useful for AI prototypes Google Drive integration Jupyter notebooks can be shared
  17. 17. DO IT YOURSELF with machine learning - step #3: machine ● Skip cloud computing (AWS, Azure, gcloud) until Step 3.. Learn to run Linux in your home! ● Pick up a Raspberry PI & SD Card from a local store. ● Install Linux (Raspbian distribution - check this guide ) and learn the basics of: ○ Terminal commands : ls, cd, md, ssh, etc.... check Linux basics on http://www.aboutdebian.com/linux.htm ● Setup your coding editor: just pick one... check Quora for ideas or just pick PyCharm for Python or Java Eclipse or Sublime Text2 or VIM if you are brave and patient. ● Learn database basics such as Postgres or MYSQL. ● Get yourself comfortable with your programming language of choice. I personally love Python (tutorial) but you may want Java (tutorial) or C# (quickstart) or whatever. ● Practice cloning existing code repositories from GitHub using GIT (tutorial) ● Algorithms can be found on github.com or gitxiv.com Raspberry PI waiting for you at local Microcenter store or on Amazon
  18. 18. Host your IPython notebook on your Raspberry PI Some how to links: https://blog.domski.pl/ipython-notebook-server-on-raspberry-pi/ or https://www.raspberrypi.org/forums/viewtopic.php?t=130450&p=870964
  19. 19. DO IT YOURSELF with machine learning - step #4: AI finally! - Learn to setup free-tier Amazon AWS instance (1 year free tier) or Microsoft Azure (free trial) or Google Cloud (free tier) for cloud computing setup and machine learning deployments - For Visual DIY AI projects: - Buy yourself a Raspberry Pi Camera (https://www.raspberrypi.org/products/camera-module-v2/) - Check Google AIY Projects https://aiyprojects.withgoogle.com/vision/ - Check Raspberry PI projects ideas here or here - For Audio DIY AI projects: - Get yourself an Amazon Echo from Amazon or any store, learn to build Alexa Skills (getting started) - Or try Google AIY project with Google Now integration - https://aiyprojects.withgoogle.com/voice/
  20. 20. Build various kits using Google AIY or Raspberry PI Or run reinforcement learning projects using games! Check https://keon.io/deep-q-learning/ and https://github.com/DanielSlater/PythonDeepLearningS amples
  21. 21. Now pick a project visual or audio for your home... and for your family....
  22. 22. Start innovating!
  23. 23. To connect with Tarek Hoteit Blog: https://tarek.computer (includes copy of the slides) Twitter: @hoteit LinkedIn: https://www.linkedin.com/in/hoteit/ Email: tarek.hoteit@tr.com

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