2019 05 11 Chicago Codecamp - Deep Learning for everyone? Challenge Accepted!

Bruno Capuano
Innovation Lead @Avanade
@elbruno | http://elbruno.com
Deep Learning for everyone?
Challenge Accepted!

@elbruno
IBM slaps patent on coffee-delivering drones (link)

@elbruno
IBM slaps patent on coffee-delivering drones
(link)

@elbruno
KEEP CALM
NO MATH
TODAY
JUST CODE

@elbruno
Excessive use
of crappy
animations
and demos!

@elbruno
Load Data
Extract
Features
Model
Consumption
Train
Model
Evaluate
Model
Prepare Your Data Build & Train Run
Machine Leaning workflow

@elbruno
Boy / Girl
Baby
Teenager
Old dude / Lady

@elbruno
Is this A or B? How much? How many? How is this organized?
Regression ClusteringClassification
Machine Learning Tasks

@elbruno1
Cognitive Services - Anomaly Detector

@elbruno
Machine Learning
Seth Juarez - @sethjuarez - Microsoft

@elbruno
Deep Learning
Seth Juarez - @sethjuarez - Microsoft

@elbruno
MNIST dataset
• The MNIST database of
handwritten digits,
• Has a training set of 60,000
examples, and a test set of 10,000
examples.
• It is a subset of a larger set
available from NIST.
• The digits have been size-
normalized and centered in a
fixed-size image.

@elbruno
Artificial Intelligence: Image Analysis

@elbruno
Why is this hard?
You see this:
But the camera sees this:

@elbruno
Machine learning and feature representations
Input
Raw image
Chihuahua
“Non”-chihuahuas
Learning
algorithm
pixel 1
pixel2
pixel 1
pixel 2

@elbruno
Input
What we want
Learning
algorithm
pixel 1
pixel2 Feature
representation
ear
eye
E.g., Does it have 2 ears? 2 eyes?
eye
ear
Raw image Features
Chihuahua
“Non”-chihuahuas

@elbruno
A machine learning subfield of learning representations of data. Exceptional effective at
learning patterns.
Deep learning algorithms attempt to learn (multiple levels of) representation by using a
hierarchy of multiple layers
If you provide the system tons of information, it begins to understand it and respond in
useful ways.
Deep Learning (DL)
https://www.xenonstack.com/blog/static/public/uploads/media/machine-learning-vs-deep-learning.png

@elbruno
TensorFlow Playground
Demo
MakeMagicHappen();
https://www.avanade.com/AI

@elbruno
Open source software library for numerical computation using data
flow graphs
Developed by Google Brain Team for machine learning and deep
learning and made open-source
TensorFlow provides an extensive suite of functions and classes that
allow users to build various models from scratch
What is TensorFlow?
These slides are adapted from the following Stanford lectures:
https://web.stanford.edu/class/cs20si/2017/lectures/slides_01.pdf
https://cs224d.stanford.edu/lectures/CS224d-Lecture7.pdf

@elbruno
import tensorflow as tf
a = tf.add(2, 3)
TF automatically names nodes
if you do not
x = 2
y = 3
print a
>> Tensor("Add:0", shape=(), dtype=int32)
Note: a is NOT 5
TensorFlow Graphs
a

@elbruno
Keras is a layer on top of TensorFlow that makes it much easier to create neural
networks.
It provides a higher level API for various machine learning routines.
Unless you are performing research into entirely new structures of deep neural
networks it is unlikely that you need to program TensorFlow directly.
Keras is a separate install from TensorFlow. To install Keras, use pip install keras
(after installing TensorFlow).
TensorFlow and Keras

@elbruno
Keras Sequential model is used to
create a feed-forward network, by
stacking layers (successive ‘add’
operations).
Shape of the input layer is specified
in the first hidden layer (or the
output layer if network had no
hidden layer).
- Input 2D image is flattened to 1D
vector.
- Dropout (with the rate 0.2) is
applied to the first hidden layer
TensorFlow for Classification: MNIST

@elbrunohttp://deeplearning.stanford.edu/wiki/index.php/Feature_extraction_using_convolution
Main CNN idea for text:
Compute vectors for n-grams and group them afterwards
A convolution layer is created by defining a kernel - which is fixed square
window of weights, and scanning this across the whole input image.
Input matrix
Convolutional
3x3 filter
Convolutional Neural Networks (CNNs)

@elbruno
Pooling layer
Pooling layers are a form of downsampling which
usually follow convolution layers in the neural network.
Applying pooling to a feature map transforms the map
into a smaller representation, and it loses some of the
exact positional information of the features. Therefore it
makes our network more invariant to small
transformations and distortions in the input image by
asking whether a feature appears in a given region of
an image (the pooling region) rather than at a specific
location.
The most common method of pooling is max-pooling,
where the the maximum value of a given region in a
feature map is taken. The example below shows max
pooling being applied with a 2×2 pooling region.
https://shafeentejani.github.io/assets/images/pooling.gif

@elbruno
Activation: ReLU
Takes a real-valued number
and thresholds it at zero
𝑅 𝑛 → 𝑅+
𝑛
Most Deep Networks use ReLU nowadays
� Trains much faster
• accelerates the convergence of SGD
• due to linear, non-saturating form
� Less expensive operations
• compared to sigmoid/tanh (exponentials etc.)
• implemented by simply thresholding a matrix at
f 𝑥 = max(0, 𝑥)
http://adilmoujahid.com/images/activation.png

@elbruno
Deep Learning for image analysis

@elbruno
Deep Neural Network: Cat vs Dog
https://becominghuman.ai/building-an-image-classifier-using-deep-learning-in-python-totally-from-a-beginners-perspective-be8dbaf22dd8

@elbruno
Cat vs Dogs Demo
MakeMagicHappen();
https://www.avanade.com/AI

@elbruno
Bruno Capuano
Innovation Lead @Avanade
@elbruno | http://elbruno.com
Q&A
Thanks!

2019 05 11 Chicago Codecamp - Deep Learning for everyone? Challenge Accepted!

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2019 05 11 Chicago Codecamp - Deep Learning for everyone? Challenge Accepted!

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