The document outlines a guide for starting a deep learning startup focusing on practical steps such as formulating an idea based on personal problems, building a trustworthy team of co-founders, and assembling a minimum viable product (MVP) while avoiding perfectionism. It emphasizes the importance of market engagement, user feedback, and leveraging existing technologies to develop a competitive product quickly. Additionally, the resource encourages active marketing and interaction with potential users to achieve product-market fit.

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

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?