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Open Source Lisbon 2018 - Filipe Barroso

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“It is the end of Smartphones with dumb applications” - Filipe Barroso

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Open Source Lisbon 2018 - Filipe Barroso

  1. 1. Mobile with 150 IQ “It is the end of Smartphones with dumb applications”
  2. 2. Artificial Intelligence? Machine Learning?
  3. 3. “At its core, Machine Learning is simply a way of achieving AI.” https://medium.com/iotforall/the-difference-between-artificial-intelligence-machine-learning-and-deep-learn ing-3aa67bff5991
  4. 4. https://machinelearningmastery.com/a-tour-of-machine-learning-algorithms
  5. 5. https://www.reddit.com/r/ProgrammerHumor/comments/9ityxi/thats_how_it_be/
  6. 6. How Can You Use Machine Learning? Models Data (1) Others Others (2) Others Yours (3) Yours Yours
  7. 7. How Can You Use Machine Learning? Three ways, with varying complexity: (1) Use a Cloud-based or Mobile API (Vision, Natural Language, etc.)
  8. 8. How Can You Use Machine Learning? Three ways, with varying complexity: (1) Use a Cloud-based or Mobile API (Vision, Natural Language, etc.) (2) Use an existing model architecture, and retrain it or fine tune on your dataset
  9. 9. How Can You Use Machine Learning? Three ways, with varying complexity: (1) Use a Cloud-based or Mobile API (Vision, Natural Language, etc.) (2) Use an existing model architecture, and retrain it or fine tune on your dataset (3) Develop your own machine learning models for new problems
  10. 10. How Can You Use Machine Learning? Three ways, with varying complexity: (1) Use a Cloud-based or Mobile API (Vision, Natural Language, etc.) (2) Use an existing model architecture, and retrain it or fine tune on your dataset (3) Develop your own machine learning models for new problems More flexible, but more effort required
  11. 11. “An open source machine learning framework for everyone.”
  12. 12. A multidimensional array. A graph of operations.
  13. 13. 14 https://twitter.com/karpathy/status/972295865187512320 Percent of ML papers that mention...
  14. 14. ● Open source Machine Learning library ● Especially useful for Deep Learning ● For research and production ● Apache 2.0 license
  15. 15. Machine Learning Hello World Image from https://github.com/mnielsen/neural-networks-and-deep-learning ?
  16. 16. What we see What the computer “sees”
  17. 17. Data Flow Graphs Computation is defined as a directed acyclic graph (DAG) to optimize an objective function ● Graph is defined in high-level language (Python) ● Graph is compiled and optimized ● Graph is executed (in parts or fully) on available low level devices (CPU, GPU) ● Data (tensors) flow through the graph ● TensorFlow can compute gradients automatically
  18. 18. Architecture ● Core in C++ ● Different front ends ○ Python and C++ today, community may add more Core TensorFlow Execution System CPU GPU Android iOS ... C++ front end Python front end ...
  19. 19. Raspberry Pi DatacentersYour laptop Android iOS Portable & Scalable
  20. 20. Making recommendations Personal marketing Smart search Image source: Play Music apps page.
  21. 21. 23 Tensorflow Lite For mobile and embedded devices.
  22. 22. TensorFlow Lite Design Interpreter Core Operation Kernels Hardware acceleration delegates Converter (to TensorFlow Lite format) Source: 2018 TensorFlow Developer Summit
  23. 23. Cross-Platform Android App (Java/C++ API) iOS App (C++ API) Converter (to TensorFlow Lite format) Trained TensorFlow Model Linux (e.g. Raspberry Pi) (Python/Java/C++ API) Source: 2018 TensorFlow Developer Summit
  24. 24. iOS developers can also use CoreML Android App (Java/C++ API) iOS App (C++ API) Converter (to TensorFlow Lite format) Trained TensorFlow Model Linux (e.g. Raspberry Pi) (Python/Java/C++ API) iOS App (Use CoreML runtime) Converter (to Core ML format) Source: 2018 TensorFlow Developer Summit
  25. 25. TensorFlow Lite in practice.. Get a model download or train Convert the model to TensorFlow Lite Write ops (If needed) Write app (Use client API) Source: 2018 TensorFlow Developer Summit
  26. 26. ● Latency: You don’t need to send a request over a network connection and wait for a response. This can be critical for video applications that process successive frames coming from a camera. ● Availability: The application runs even when outside of network coverage. ● Speed: New hardware specific to neural networks processing provide significantly faster computation than with general-use CPU alone. ● Privacy: The data does not leave the device. ● Cost: No server farm is needed when all the computations are performed on the device. Benefits https://developer.android.com/ndk/guides/neuralnetworks/
  27. 27. ● System Utilization: Evaluating neural networks involve a lot of computation, which could increase battery power usage. ● Application Size: Models may take up multiple megabytes of space. Trade-offs https://developer.android.com/ndk/guides/neuralnetworks/
  28. 28. tensorflow.org tensorflow.org/lite/ github.com/tensorflow Guides, codelabs, videos MNIST for Beginners, goo.gl/tx8R2b TF Learn Quickstart, goo.gl/uiefRn TensorFlow for Poets, goo.gl/bVjFIL ML Recipes, goo.gl/KewA03 TensorFlow and Deep Learning without a PhD, goo.gl/pHeXe7 Online Various online courses in Coursera, Udacity... Learning More
  29. 29. Hands-On Machine Learning with Scikit-Learn and TensorFlow https://www.oreilly.com/
  30. 30. Aug 13 https://goo.gl/nwV2Vq TensorFlow 2.0 announcement
  31. 31. Support Your Local Communities https://www.meetup.com
  32. 32. … Google Developer Group Organizer; … Remote Developer; … Speaker; … Community Advocate; … Mobile Developer … Machine Learning Engineer; … Standup Comedian; Thank you Filipe Barroso - Acceptto Twitter @ABarroso Ask Me Anything about Communities!

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