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C O R E M L A N D
C O M P U T E R V I S I O N
@ M I L L A N I M I X
H U N I M I X U U K M U WA N
L E O N A R D O - I S R A E L M I L L Á N - G A R C Í A @millanimix
• Mexicano, Tenochca, SkyAnahuacwalker
• Student of the Pre-Hispanic Tradition
• Rusty Researcher of Computer Vision
• Objective-C developer (Oldie but goodie)
• ¡Ah! I currently work as Project Manager
C O R E M L
• Integrate machine
learning models into your
app.
• A trained model is the
result of applying a
machine learning
algorithm to a set of
training data.
• The model makes
predictions based on new
input data.
https://developer.apple.com/documentation/coreml
E X A M P L E
Link example
H O W D O I T D O ?
C O R E M L
C O R E M L
• Core ML Supports:
• Image analysis.
• Foundation NLP.
• Learned decision
trees.
https://developer.apple.com/documentation/coreml
V I S I O N
N AT U R A L V S A R T I F I C I A L
6 to 7 millions of cones
120 millions of rods
iPhone X 12MP
V I S I O N F R A M E W O R K
C O M P U T E R V I S I O N
V I S I O N F R A M E W O R K
• Still Image Analysis.
• Image Sequence
Analysis.
• Object Tracking.
• Rectangle Detection.
• Face Detection.
• Barcode Detection.
• Text Detection.
• Horizon Detection.
• Image Alignment.
• Machine-Learning
Image Analysis.
• Coordinate
Conversion.
C O R E M L
• Core ML Tools: Python package
• Machine learning models
(.mlmodel):
• Neural networks.
• Tree ensembles.
• Support vector machines.
• Generalized linear models.
• Takes advantages of the CPU
and GPU
C O R E M L
• BNNS (Basic Neural Network
Subroutines)
• Accelerate Framework: A
collection of math functions.
• CPU’s fast vector
instructions.
• MPSCNN: Metal Performance
Shaders - Convolutional Neural
Networks
• Compute kernels that run
on the GPU
https://developer.apple.com/documentation/coreml
L E A R N I N G
N E U R A L N E T W O R K
H U M A N
N E U R A L N E T W O R K
A R T I F I C I A L
H U M A N N E U R A L N E T W O R K S
• Neuron
• Electrically excitable cell.
• Receives, processes and
transmits information
through electrical and
chemical signals.
• Human Brain
• 1508 g.
• 86 billion neurons.
A R T I F I C I A L N E U R A L N E T W O R K S
• Input layer
• Hidden layers
• Output layer
A 1 1 B I O N I C
H A R D WA R E
A 1 1 B I O N I C
• System on a Chip (SoC).
• 64-bit ARM / 4.3 billion
transistors.
• iPhone 8 / Plus & X.
• Two performance cores /
Four high-efficiency cores.
• Apple-designed GPU with
three-core design.
S U M M A RY
A R T I F I C I A L I N T E L L I G E N C E
S U M M A RY
A R T I F I C I A L I N T E L L I G E N C E
Q U E S T I O N S ?

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Core ML and Computer Vision

  • 1. C O R E M L A N D C O M P U T E R V I S I O N @ M I L L A N I M I X
  • 2. H U N I M I X U U K M U WA N L E O N A R D O - I S R A E L M I L L Á N - G A R C Í A @millanimix • Mexicano, Tenochca, SkyAnahuacwalker • Student of the Pre-Hispanic Tradition • Rusty Researcher of Computer Vision • Objective-C developer (Oldie but goodie) • ¡Ah! I currently work as Project Manager
  • 3. C O R E M L • Integrate machine learning models into your app. • A trained model is the result of applying a machine learning algorithm to a set of training data. • The model makes predictions based on new input data. https://developer.apple.com/documentation/coreml
  • 4. E X A M P L E Link example
  • 5. H O W D O I T D O ? C O R E M L
  • 6. C O R E M L • Core ML Supports: • Image analysis. • Foundation NLP. • Learned decision trees. https://developer.apple.com/documentation/coreml
  • 7. V I S I O N N AT U R A L V S A R T I F I C I A L 6 to 7 millions of cones 120 millions of rods iPhone X 12MP
  • 8. V I S I O N F R A M E W O R K C O M P U T E R V I S I O N
  • 9. V I S I O N F R A M E W O R K • Still Image Analysis. • Image Sequence Analysis. • Object Tracking. • Rectangle Detection. • Face Detection. • Barcode Detection. • Text Detection. • Horizon Detection. • Image Alignment. • Machine-Learning Image Analysis. • Coordinate Conversion.
  • 10. C O R E M L • Core ML Tools: Python package • Machine learning models (.mlmodel): • Neural networks. • Tree ensembles. • Support vector machines. • Generalized linear models. • Takes advantages of the CPU and GPU
  • 11. C O R E M L • BNNS (Basic Neural Network Subroutines) • Accelerate Framework: A collection of math functions. • CPU’s fast vector instructions. • MPSCNN: Metal Performance Shaders - Convolutional Neural Networks • Compute kernels that run on the GPU https://developer.apple.com/documentation/coreml
  • 12. L E A R N I N G
  • 13. N E U R A L N E T W O R K H U M A N
  • 14. N E U R A L N E T W O R K A R T I F I C I A L
  • 15. H U M A N N E U R A L N E T W O R K S • Neuron • Electrically excitable cell. • Receives, processes and transmits information through electrical and chemical signals. • Human Brain • 1508 g. • 86 billion neurons.
  • 16. A R T I F I C I A L N E U R A L N E T W O R K S • Input layer • Hidden layers • Output layer
  • 17. A 1 1 B I O N I C H A R D WA R E
  • 18. A 1 1 B I O N I C • System on a Chip (SoC). • 64-bit ARM / 4.3 billion transistors. • iPhone 8 / Plus & X. • Two performance cores / Four high-efficiency cores. • Apple-designed GPU with three-core design.
  • 19.
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  • 21. S U M M A RY A R T I F I C I A L I N T E L L I G E N C E
  • 22. S U M M A RY A R T I F I C I A L I N T E L L I G E N C E
  • 23. Q U E S T I O N S ?