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

2. Mobile with 150 IQ
“It is the end of Smartphones with dumb applications”

3. Artificial Intelligence?
Machine Learning?

4. “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

5. https://machinelearningmastery.com/a-tour-of-machine-learning-algorithms

6. https://www.reddit.com/r/ProgrammerHumor/comments/9ityxi/thats_how_it_be/

7. How Can You Use Machine Learning?
Models Data
(1) Others Others
(2) Others Yours
(3) Yours Yours

8. How Can You Use Machine Learning?
Three ways, with varying complexity:
(1) Use a Cloud-based or Mobile API (Vision, Natural Language,
etc.)

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

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

11. 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

12. “An open source machine learning framework for
everyone.”

13. A multidimensional array.
A graph of operations.

14. 14
https://twitter.com/karpathy/status/972295865187512320
Percent of ML papers that mention...

15. ● Open source Machine
Learning library
● Especially useful for
Deep Learning
● For research and production
● Apache 2.0 license

16. Machine Learning Hello World
Image from https://github.com/mnielsen/neural-networks-and-deep-learning
?

17. What we see What the computer “sees”

19. 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

20. 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 ...

21. Raspberry
Pi
DatacentersYour laptop Android iOS
Portable & Scalable

22. Making
recommendations
Personal marketing
Smart search
Image source: Play Music apps page.

23. 23
Tensorflow Lite
For mobile and embedded devices.

24. TensorFlow Lite Design
Interpreter
Core
Operation
Kernels
Hardware acceleration
delegates
Converter
(to TensorFlow
Lite format)
Source: 2018 TensorFlow Developer Summit

25. 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

26. 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

27. 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

28. ● 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/

29. ● 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/

30. 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

31. Hands-On Machine Learning with
Scikit-Learn and TensorFlow
https://www.oreilly.com/

32. Aug 13
https://goo.gl/nwV2Vq
TensorFlow 2.0 announcement

33. Support Your Local Communities
https://www.meetup.com

34. … 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!