The document discusses machine learning and TensorFlow. It provides three ways to get started with machine learning of varying complexity: using cloud APIs, retraining existing models, or developing new models. It then discusses Google Cloud machine learning APIs for vision, natural language, speech and translation. It provides examples of using the vision API and mobile vision API. It introduces TensorFlow as an open source machine learning library for research and production with Python and C++ frontends.

1. +Israel Blancas
@iblancasa
TensorFlow
La IA detrás de Google
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2. How Can You Get Started with 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
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3. Cloud Machine Learning APIs
See, Hear and Understand the world
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4. Cloud
Natural Language
Cloud
Speech
Cloud
Translate
Cloud
Vision
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5. 55
Vision API
https://cloud.google.com/vision/
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6. Faces
Faces, facial landmarks, emotions
OCR
Read and extract text, with
support for > 10 languages
Label
Detect entities from furniture to
transportation
Logos
Identify product logos
Landmarks & Image Properties
Detect landmarks & dominant
color of image
Safe Search
Detect explicit content - adult,
violent, medical and spoof
Cloud Vision API
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7. API Usage: Detect Objects in an Image
Image Detected
Items
Vision API
Create JSON
request with the
image or pointer
to an image
Process
the JSON
response
Call the
REST API1 2 3
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8. 88
Cloud Natural
Language API
https://cloud.google.com/natural-language/
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9. Confidential & ProprietaryGoogle Cloud Platform 9
Cloud Natural Language API
Extract sentence, identify parts of
speech and create dependency parse
trees for each sentence.
Identify entities and label by types such
as person, organization, location, events,
products and media.
Understand the overall sentiment of a
block of text.
Syntax Analysis Entity Recognition
Sentiment Analysis
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10. 1010
Cloud Speech API
Demohttps://cloud.google.com/speech/
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11. Confidential & ProprietaryGoogle Cloud Platform 11
Cloud Speech API
Automatic Speech Recognition (ASR)
powered by deep learning neural
networking to power your
applications like voice search or
speech transcription.
Recognizes over 80
languages and variants
with an extensive
vocabulary.
Returns partial
recognition results
immediately, as they
become available.
Filter inappropriate
content in text results.
Audio input can be captured by an application’s
microphone or sent from a pre-recorded audio
file. Multiple audio file formats are supported,
including FLAC, AMR, PCMU and linear-16.
Handles noisy audio from many
environments without requiring
additional noise cancellation.
Audio files can be uploaded in the
request and, in future releases,
integrated with Google Cloud
Storage.
Automatic Speech Recognition Global Vocabulary Inappropriate Content
Filtering
Streaming Recognition
Real-time or Buffered Audio Support Noisy Audio Handling Integrated API
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12. Mobile Vision API
Providing on-device vision for applications
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13. Face API
faces, facial landmarks, eyes
open, smiling
Barcode API
1D and 2D barcodes
Text API
Latin-based text / structure
Common Mobile Vision API
Support for fast image and video on-device detection and tracking.
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14. Googly Eyes Android App
Video credit Google
1. Create a face detector for facial landmarks (e.g., eyes)
3. For each face, draw the eyes
FaceDetector detector = new FaceDetector.Builder()
.setLandmarkType(FaceDetector.ALL_LANDMARKS)
.build();
SparseArray<Face> faces = detector.detect(image);
for (int i = 0; i < faces.size(); ++i) {
Face face = faces.valueAt(i);
for (Landmark landmark : face.getLandmarks()) {
// Draw eyes
2. Detect faces in the image
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15. Face API
Photo credit developers.google.com/vision
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16. Easy to use Java API
image detected
items
Detector
1. Create a detector object
2. detectedItems = detector.detect(image)
Photo credit developers.google.com/vision
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17. Text Detection
Latin based language
Understand text structure
Photo credit Getty Images
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18. Text Structure
Blocks
Lines
Words
Lines
Words Words Words
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19. Barcode Detection
1D barcodes
EAN-13/8
UPC-A/E
Code-39/93/128
ITF
Codabar
2D barcodes
QR Code
Data Matrix
PDF-417
AZTEC
UPC
DataMatrix
QR Code
PDF 417
Video and image credit Google
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20. Combined Vision & Translation
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21. ● Open source Machine
Learning library
● Especially useful for
Deep Learning
● For research and
production
● Apache 2.0 license
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22. 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 ...
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23. Raspberry
Pi
DatacentersYour laptop Android iOS
Portable & Scalable
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24. /Serving
Open-source solution for serving trained models
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25. From the whitepaper: “TensorFlow is an interface for expressing machine
learning algorithms, and an implementation for executing such algorithms.”
In short: TensorFlow is Theano++.
● Symbolic ML dataflow framework that compiles to native / GPU code
What is TensorFlow?
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26. 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
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27. Graph?
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28. Variables are 0-ary stateful nodes
which output their current value.
(State is retained across multiple executions
of a graph.)
(parameters, gradient stores, eligibility traces, …)
Graph?
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29. Placeholders are 0-ary nodes whose
value is fed in at execution time.
(inputs, variable learning rates, …)
Graph?
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30. Mathematical operations:
MatMul: Multiply two matrix values.
Add: Add elementwise (with broadcasting).
ReLU: Activate with elementwise rectified
linear function.
Graph?
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31. In code, please!
1. Create model weights, including
initialization
a. W ~ Uniform(-1, 1); b = 0
2. Create input placeholder x
a. m * 784 input matrix
3. Create computation graph
import tensorflow as tf
b = tf.Variable(tf.zeros((100,)))
W = tf.Variable(tf.random_uniform((784, 100),
-1, 1))
x = tf.placeholder(tf.float32, (None, 784))
h_i = tf.nn.relu(tf.matmul(x, W) + b)
1
2
3
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32. So far we have defined a graph.
We can deploy this graph with a session: a binding
to a particular execution context (e.g. CPU, GPU)
How do we run it?
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33. Getting output
sess.run(fetches, feeds)
Fetches: List of graph nodes.
Return the outputs of these
nodes.
Feeds: Dictionary mapping from
graph nodes to concrete values.
Specifies the value of each graph
node given in the dictionary.
import numpy as np
import tensorflow as tf
b = tf.Variable(tf.zeros((100,)))
W = tf.Variable(tf.random_uniform((784,
100),-1, 1))
x = tf.placeholder(tf.float32, (None, 784))
h_i = tf.nn.relu(tf.matmul(x, W) + b)
1
2
3
sess = tf.Session()
sess.run(tf.initialize_all_variables())
sess.run(h_i, {x: np.random.random(64, 784)})
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34. 1. Build a graph
a. Graph contains parameter specifications, model architecture, optimization process, …
2. Initialize a session
3. Fetch and feed data with
Session.run
a. Compilation, optimization, etc. happens at this step — you probably won’t notice
Basic flow
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35. Questions?
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36. tensorflow.org
github.com/tensorflow
Want to learn more?
Udacity class on Deep Learning, goo.gl/iHssII
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
What's Next
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37. +Israel Blancas
@iblancasa
TensorFlow
La IA detrás de Google
#DevFestGRX
GDG Granada