TensorFlow presentation for the BAM Meetup group.

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Justin Brandenburg
Data Scientist, Professional Services
May 16th, 2017

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What are we working with?
• Machine Learning and Deep Learning
• Intro to TensorFlow
• Demo/DNN
• Practical Applications
• Demo/RNN

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D E E P
L E A R N I N G

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No Silver Bullets

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Machine Learning
Machine learning is a type of artificial intelligence (AI) that
provides computers with the ability to learn without being
explicitly programmed.
ML allows a computer to make predictions on data (usually
based on historical data)

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Deep Learning
Deep learning is a particular subset of ML methodologies using
artificial neural networks (ANN)
• Successfully applied to so many different domains (image, text, video,
speech, and vision)
• Success of DL is also due to the availability of more training data (such as
ImageNet for images) and the relatively low-cost availability of storage and
increase in computational power

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Network Architecture

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Deep Learning Implementations
Convolutional
Neural
Networks
Deep
Neural
Networks
Recurrent
Neural
Networks
providing lift for
classification and
forecasting models
feature extraction
and classification of
images
for sequence of
events (sentences
or time series)

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TENSORFLOW

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TensorFlow
TensorFlow is an open source software library for numerical
computation using data flow graphs
• Developed by Google, released to open source community in Nov 2015 and
quickly became one of the most popular deep learning frameworks
• Two months after its release it had already become the most popular forked
ML GitHub repository
• Built on C++ with a Python interface

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What is a Tensor?
A Tensor is a n-dimensional matrix
• 1D is a vector
• 2D (M x M) matrix/tensor is a square array of numbers (m numbers tall and m
numbers wide)
• M x M x M tensor is a cube array (m tall, m wide, m deep)

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Computational Graphs
When TF performs an operation, it does so using a data flow
graph (computational graph)
• Similar to a flow chart, it is a directed graph that is made up of:
– Nodes, representing operations (ex: sum of two integers)
– Directed Arcs, representing data on which operations are performed

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Programming Model
A TF program is made up of two phases:
• Construction of the computational graph
• Running a session, which is performed for the operations defined in the
graph
– Resulting data collection and analysis

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Example

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Construction The session object creates the
computational graph
Variable placeholders allow
for the construction of the
operations without data
Model we want to compute

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Execute

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Data Collection & Analysis

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TensorBoard

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TensorBoard

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2 Layer DNN in
Jupyter Notebook

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MNIST Dataset
• Database of handwritten digits made up of a training set of
60,000 examples, plus a test set of 10000 example

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Practical
Applications

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When to use?
TF can perform many machine learning algorithms such as
• Linear Regression
• Logistic Regression
• Support Vector Machines
• Nearest Neighbor Models
• NLP
But does performing your machine learning using TensorFlow
offer any more value than Spark MLlib?

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TF vs MLlib
Stability/Conveyance Rapidly changing version to version Rapid Incorporation of new capabilities
Big Data Environment Distributed Execution is not easy Natural Distributed Execution
Technical Docs Documentation is dense, very technical Excellent Documentation
Reliability Stable performance Occasionally Unstable performance
Analytical Competitive Advantage Deep Learning/Neural Network Focused General Machine Learning techniques
Languages C++, Python Scala, Python, Java, R
Image Recognition Strong Performance on Images Pretty Good, getting better

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TF at Scale
If you are already running machine learning on a production
cluster, how do you incorporate TF?
• TF runs on CPUs and GPUs
• A data pipeline can be utilized by having Spark perform the
ETL
• A good distributed data layer (MapR-FS ) is needed to feed
in data and to receive exported results
• Can leverage a GPU cluster for processing and have TF in
containers with Kubernetes to manage the GPU allocation

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RNN Time Series in
Jupyter Notebook

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Recurrent Neural Network at a Glance
• A neural network that can be used when your data is treated as
a sequence, where the particular order of the data-points
matter
• Sometimes, the input is a sequence and the output is a single
vector, or the other way around.

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Recurrent Neural Network Topology
Unrolling through time

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Other RNN Implementations

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Points to Remember
• There is no ML silver bullet
• Normally an ensemble of models is necessary
• TF is just one ML tool among many (but a great one)
• Choosing the right one depends on your problem
– Ex: Supervised or Unsupervised
• Start with simplest methods first
• If logistic regression works, use it
• Tools are never used in isolation, the platform matters!

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Q&A
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