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Start a deep learning startup - tutorial

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Mostapha Benhenda
Mostapha Benhenda

A step-by-step tutorial to start a deep learning startup. Deep learning is a specialty of artificial intelligence, based on neural networks. I explain how I launched my face recognition startup: Mindolia.com

Small Business & Entrepreneurship
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How to start a deep learning startup, NOT from scratch Mostapha Benhenda, Mindolia Kyiv deep learning meetup, 13 september 2016
What is deep learning? ● Specialty of machine learning, which uses 'deep' neural networks, i.e. with many (>3) layers. ● No need to really understand what is 'deep learning' in order to use it, just apply it: ● Applied mostly to understand images, videos, languages (text, DNA...) and speech.
Why starting a startup? ● No experience, no job ? Just hire yourself! ● Startup = easiest way to get a real job experience, with an awesome boss: you! ● Acqui-hire >> hire ● Startup for ML beginners >>> Coursera, Kaggle ● ML for startup: easier than ML for big company (less data, less optimization needed) ● Startup more difficult to start later: higher opportunity cost (better job offers with experience): now or never!
How to start a DL startup: easy Deep learning startup = startup using deep learning. You need: 1. Idea 2. Team 3. Product using deep learning 4. Market
● These 4 things: done quickly, and in parallel ● Avoid perfectionnism! ● Improve the bottleneck, the weakest link
1. Elaborate an idea ● best idea: from your own problems ● In my case (facial recognition): ringing doorbell= noise pollution ● Focus on customer pain ● Don't think too much: idea is only a starting point ● No idea → clone other startups (see Angellist, Crunchbase...) ● See my list of 19 ideas: https://docs.google.com/presentation/d/1Z-CPIGbSSTOm_EaqS5ks1V ...any questions?
2. Build a team ● Ideal team: 2 or 3 co-founders (Hipster + Hacker + Hustler) ● Criteria of Minimal Viable Co-founders: trust, motivation and skills ● No co-founder: start as a single founder ● Human co-founders disrupted by 'AI co-founders': AWS, Google, Stackoverflow, Quora, blogs....
3. Assemble a deep learning product ● Like IKEA: use ready-made parts
Minimal Viable Product (MVP): ● Design: sober and clean ● Code: quick and dirty
MVP= Deep learning+ Web app Deep learning feature: ● Transfer learning (1 line of code+ little data) ● Open-source API: OpenFace, DeepDetect... ● Commercial API (Google, smaller companies...): why not, but be careful of locking ● Don't start from scratch!!! ...any questions?
Web/mobile application ● Build your app locally first, then deploy ● Use LAMP: Linux Apache Mysql Python ● In my product, I used Twisted instead of Apache because of live streaming ● Deployment: AWS or others (Microsoft, Google, Heroku...) ● Debugging: use Google, Stackoverflow, and Rubber Duck
4. Go to the market ● Code, technology: cheap moneypot ● Users, customers: valuable bees
Example: Uber Clone ● Cost: 2000 dollars with a freelancer
Original Uber ● Uber inc. is valued to 66 Billion dollars
Difference: ● Original Uber: 66 Million monthly trips ● Uber clone: zero trip. ● Conclusion: don't stop at coding, continue and find users!!
Product/market fit ● Talk to potential users ● Monitor metrics, watch behavior ● Marketing, get visibility for your brand: communicate with blogs: http://tinyurl.com/juy7exc ● Video clips: https://www.youtube.com/watch?v=81btY-pjYeA ● ….any questions?
More advertising (for the meetup): Hackathon 'Smart-techno' this weekend at Gulliver mall 24th floor, hands-on practice of this tutorial. ● Meetup agenda and suggestions: http://tinyurl.com/h5rl5ze ● Including 2 'orphan' Tensorflow tutorials, waiting for their instructors! Adopt them, they are cute! ● Incentive: IF enough people study the tutorials VERY seriously (i.e. able to give useful feedback), THEN we will invite relevant experts for remote Q&A sessions! …any questions?

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Start a deep learning startup - tutorial

  • 1. How to start a deep learning startup, NOT from scratch Mostapha Benhenda, Mindolia Kyiv deep learning meetup, 13 september 2016
  • 2. What is deep learning? ● Specialty of machine learning, which uses 'deep' neural networks, i.e. with many (>3) layers. ● No need to really understand what is 'deep learning' in order to use it, just apply it: ● Applied mostly to understand images, videos, languages (text, DNA...) and speech.
  • 3. Why starting a startup? ● No experience, no job ? Just hire yourself! ● Startup = easiest way to get a real job experience, with an awesome boss: you! ● Acqui-hire >> hire ● Startup for ML beginners >>> Coursera, Kaggle ● ML for startup: easier than ML for big company (less data, less optimization needed) ● Startup more difficult to start later: higher opportunity cost (better job offers with experience): now or never!
  • 4. How to start a DL startup: easy Deep learning startup = startup using deep learning. You need: 1. Idea 2. Team 3. Product using deep learning 4. Market
  • 5. ● These 4 things: done quickly, and in parallel ● Avoid perfectionnism! ● Improve the bottleneck, the weakest link
  • 6. 1. Elaborate an idea ● best idea: from your own problems ● In my case (facial recognition): ringing doorbell= noise pollution ● Focus on customer pain ● Don't think too much: idea is only a starting point ● No idea → clone other startups (see Angellist, Crunchbase...) ● See my list of 19 ideas: https://docs.google.com/presentation/d/1Z-CPIGbSSTOm_EaqS5ks1V ...any questions?
  • 7. 2. Build a team ● Ideal team: 2 or 3 co-founders (Hipster + Hacker + Hustler) ● Criteria of Minimal Viable Co-founders: trust, motivation and skills ● No co-founder: start as a single founder ● Human co-founders disrupted by 'AI co-founders': AWS, Google, Stackoverflow, Quora, blogs....
  • 8. 3. Assemble a deep learning product ● Like IKEA: use ready-made parts
  • 9. Minimal Viable Product (MVP): ● Design: sober and clean ● Code: quick and dirty
  • 10. MVP= Deep learning+ Web app Deep learning feature: ● Transfer learning (1 line of code+ little data) ● Open-source API: OpenFace, DeepDetect... ● Commercial API (Google, smaller companies...): why not, but be careful of locking ● Don't start from scratch!!! ...any questions?
  • 11. Web/mobile application ● Build your app locally first, then deploy ● Use LAMP: Linux Apache Mysql Python ● In my product, I used Twisted instead of Apache because of live streaming ● Deployment: AWS or others (Microsoft, Google, Heroku...) ● Debugging: use Google, Stackoverflow, and Rubber Duck
  • 12.
  • 13. 4. Go to the market ● Code, technology: cheap moneypot ● Users, customers: valuable bees
  • 14. Example: Uber Clone ● Cost: 2000 dollars with a freelancer
  • 15. Original Uber ● Uber inc. is valued to 66 Billion dollars
  • 16. Difference: ● Original Uber: 66 Million monthly trips ● Uber clone: zero trip. ● Conclusion: don't stop at coding, continue and find users!!
  • 17. Product/market fit ● Talk to potential users ● Monitor metrics, watch behavior ● Marketing, get visibility for your brand: communicate with blogs: http://tinyurl.com/juy7exc ● Video clips: https://www.youtube.com/watch?v=81btY-pjYeA ● ….any questions?
  • 18. More advertising (for the meetup): Hackathon 'Smart-techno' this weekend at Gulliver mall 24th floor, hands-on practice of this tutorial. ● Meetup agenda and suggestions: http://tinyurl.com/h5rl5ze ● Including 2 'orphan' Tensorflow tutorials, waiting for their instructors! Adopt them, they are cute! ● Incentive: IF enough people study the tutorials VERY seriously (i.e. able to give useful feedback), THEN we will invite relevant experts for remote Q&A sessions! …any questions?