machine intelligence at google scale: tensorflow
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Machine Intelligence at Google Scale:
TensorFlow
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Data is Everything.How well you use your data can determine the degree of your success.
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What is Machine Learning?
data algorithm insight
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Beach
Woman
Pool
Coast
Water
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Google Translate
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Human: Someone is using a small grill to melt his sandwich.
Neural Model: A person is cooking some food on a grill.
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One example (of 100s): “Neural Art” in TensorFlow
An implementation of "A neural algorithm of Artistic style" in TensorFlow, for
● Introductory, hackable demos for TensorFlow, and
● Demonstrating the use of importing various Caffe cnn models (VGG and illustration2vec) in TF.
github.com/woodrush/neural-art-tf
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Growing Use of Deep Learning at GoogleNumber of directories containing model description files
Uniq
ue P
roje
ct D
irect
orie
s
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3
2000
1500
1000
500
0
Across many products/areas● Apps● Maps● Photos● Gmail● Speech● Android● YouTube● Translation● Robotics Research● Image Understanding● Natural Language
Understanding● Drug Discovery
2012 2013 2014 2015
Q4
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The Machine Learning Spectrum
TensorFlow Cloud Machine Learning Machine Learning APIs
Data Scientist
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TensorFlow
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● Google's open source library for machine intelligence
● Tensor: N-dimensional array
● Flow: data flow computation framework (like MapReduce)
● tensorflow.org launched in Nov 2015○ Most popular Machine Learning library○ 20,000+ stars / 7000+ forks ○ 165 contributors / > 3000 commits
What is TensorFlow?
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Portable● Training on:
○ Data Center
○ CPUs, GPUs and etc
● Running on:
○ Mobile phones
○ IoT devices
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Computation as a Dataflow Graph
Graph of Nodes, also called operations (ops)
MatMul
Add Softmax
biases
weights
inputs
targets
Xent
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Dataflow Graph (forward)
Edges are N-dimensional arrays: Tensors
MatMul
Add Softmax
biases
weights
inputs
targets
Xent
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Dataflow Graph (backward graph and updates)Backward graph and update are added automatically to graph
Add Mul
biases
...
learning rate
−=...
'Biases' is a variable −= updates biasesSome ops compute gradients
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Dataflow Graph ● In practice often very complex with 100s
or 1000s of nodes and edges● “Inference” means to execute just the
forward path of the graph
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Example: Logistic Regression1. N examples of data
○ Input xn○ Target tn
2. Model with parameters (weights W, biases b)○ yn = Softmax(W * xn + b)
3. CrossEntropy loss to minimize○ loss = -Sum(tn * ln(yn))
4. Minimization by Gradient Descent○ requires gradients of loss with respect to parameters W, b
● TensorFlow does automatic differentiation● Gradient calculation done by TF once model & loss defined
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Visualizing learning
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Visualizing graphs
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Portable● Training on:
○ Data Center
○ CPUs, GPUs and etc
● Running on:
○ Mobile phones
○ IoT devices
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TensorBoard: visualization tool
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The Machine Learning Spectrum
TensorFlow Cloud Machine Learning Machine Learning APIs
Application Developer
Data Scientist
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Cloud Machine Learning Platform
Pre-Processing TrainingML Authoring
ML Analysis
Hosted Model Monitoring & Logging
Training
Prediction
● Intelligent Text Processing● Image Feature Processing
● Managed Infrastructure● Scalable Algorithms● HW Acceleration
Evaluation
● Detailed Model Metrics● In-process model viz● No-ops experience
Batch
Real-Time
input
output
● Hosted Model experience● AutoScaling● Anytime access
Data
Console
Model & Job Mgmt
Datalab
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Can I Hug That?
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Can I Hug That?
Images
Labels
Trained Classifier
Question
Hug / Not Hug
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Demo
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Confidential & ProprietaryGoogle Cloud Platform 28
Easy to use API: pass in an image, we give you insight:
Google Cloud Vision API
Label Detection OCR
Explicit Content Detection
Facial Detection
Landmark Detection
Logo Detection