You Can Do Deep Learning!

It’s 2018, and you don’t need a PhD to do deep learning. This talk will empower you to build your first deep learning project using Python and open-source tools. In other domains, like web development, we leverage frameworks that give us high-level abstractions to work with, and I will show you that deep learning doesn’t have to be different.

Transcript

1. You Can Do Deep Learning! By William Horton
3. Image Classifier cat? dog?
5. Other applications
6. Who am I? • Backend Engineer, Data Team at Compass • Python • Deep Learning
8. Let’s dive in!
10. Deep Learning
14. $ gem install rails $ rails new blog $ cd blog $ bin/rails server $ pip install Django $ django-admin startproject blog $ cd blog $ python manage.py runserver
17. Magic!
18. Deep Learning
20. Common roadblocks What about the math? Having to learn everything about everything before you even let yourself start Thinking you can’t do this because you don’t have a PhD
22. the fast.ai formula:
23. Math is important Theory is important But why not start with the knowledge and skills that you have?
24. https://cloud.google.com/blog/big-data/2016/08/how-a-japanese-cucumber-farmer-is- using-deep-learning-and-tensorflow
25. Use what you know!
28. You know how to translate ideas into working code.
30. You know how to read the documentation and source of libraries.
32. You’ve probably spent hours of your life debugging.
34. Putting it together
36. Kaggle Kernels
38. fast.ai library
39. Communities fast.ai forums Pytorch forums Kaggle discussion threads AI Saturdays Medium Twitter
40. http://tools.google.com/seedbank/
41. What can I do?
42. Near term questions What happens if I train for longer? What happens if I change the learning rate? Can I apply this to another dataset? Can I use a different architecture? What is training loss? What is validation loss?
43. Understand, implement, and explain https://medium.com/@radekosmulski/do-smoother-ar eas-of-the-error-surface-lead-to-better-generalization- b5f93b9edf5b http://teleported.in/posts/cyclic-learning-rate/
44. https://medium.com/@hortonhearsafoo/adding-a-cutting-edge-deep-learning-train ing-technique-to-the-fast-ai-library-2cd1dba90a49
45. Win an Emmy?
48. Tackle your business problems
49. Take on the giants https://www.theverge.com/2018/5/7/17316010/fast-ai-speed-test-stanford-dawnbe nch-google-intel
52. Now get started! (And let me know: @hortonhearsafoo)
53. Image Credits Terminator: https://www.sideshowtoy.com/assets/products/300157-terminator-t-800-endoskeleton/lg/terminator-2-terminator-t-800-endoskeleton-m aquette-sideshow-300157-17.jpg Dog: https://i.ytimg.com/vi/SfLV8hD7zX4/maxresdefault.jpg Cat: https://upload.wikimedia.org/wikipedia/commons/thumb/3/3a/Cat03.jpg/1200px-Cat03.jpg ImageNet graph: https://www.economist.com/special-report/2016/06/25/from-not-working-to-neural-networking Google Translate: https://translate.google.com/ AlphaGo: https://deepmind.com/research/alphago/ Compass: https://www.compass.com/ Tensorflow: screenshotted from https://www.tensorflow.org/ Coursera: from https://www.facebook.com/Coursera/ Khan Academy: https://cdn.kastatic.org/images/khan-logo-dark-background.png Kaggle: https://en.wikipedia.org/wiki/Kaggle Udacity: https://d20vrrgs8k4bvw.cloudfront.net/images/open-graph/udacity.png Roadblock: https://thesandwichedman.files.wordpress.com/2017/03/roadblock.jpg?w=519 You Shall Not Pass: https://coldcallcoach.net/donts-grappling-gatekeeper/ django: https://en.wikipedia.org/wiki/Django_(web_framework) Rails: screenshotted from https://www.youtube.com/watch?v=Gzj723LkRJY DHH: http://david.heinemeierhansson.com/images/me.jpg
54. You’re on rails: screenshotted from https://guides.rubyonrails.org/getting_started.html Magic: https://recruitingtools.com/wp-content/uploads/sites/2/2017/07/magic-wand.jpg fastai: http://www.fast.ai/ fastai video: https://www.youtube.com/watch?v=Th_ckFbc6bI fastai formula: screenshotted from http://course.fast.ai/ python: https://www.python.org/ jira ticket: http://www.taigeair.com/JIRA-Ticket mockup: https://wireframesketcher.com/sample-mockups.html keras documentation: https://keras.io/getting-started/functional-api-guide/ pytorch github: https://github.com/pytorch/pytorch stack trace: http://i45.tinypic.com/nzm99h.jpg stackoverflow: https://stackoverflow.com/ gpu: https://www.notebookcheck.net/fileadmin/_processed_/d/3/csm_GeForce_GTX_1080ti_3qtr_top_left__f25b948c6c.jpg data: http://metaltechalley.com/wp-content/uploads/2017/09/data.jpg jupyter: http://jupyter.org/assets/try/jupyter.png tensorflow: https://upload.wikimedia.org/wikipedia/commons/thumb/1/11/TensorFlowLogo.svg/2000px-TensorFlowLogo.svg.png keras: https://keras.io/ scikit-learn: https://twitter.com/scikit_learn pytorch: https://pytorch.org/ emmy nominations: http://www.emmys.com/awards/nominees-winners/2018/outstanding-creative-achievement-in-interactive-media-within-a-scripted-progr am not hotdog app: http://www.bgr.in/news/the-ridiculous-not-hotdog-app-is-real-and-here-are-6-other-apps-if-you-love-it/ not hotdog linkedin post: https://www.linkedin.com/feed/update/urn:li:activity:6423300978275082240/ dawnbench: https://dawn.cs.stanford.edu/benchmark/