Deep Learning for Sales Professionals

Dr. Miguel Fierro

Data Scientist at Microsoft

@miguelgfierrohttps://miguelgfierro.com

Intelligent Cloud GBB Ready | Lisbon Sept 2017

D e e p l e a r n i n g s o l u t i o n s D e e p l e a r n i n g t e c n i q u e s

Intelligent Cloud GBB Ready Lisbon 2017 - Dr. Miguel Fierro @miguelgfierro

source: wikipedia

“Our strategy is to build best-in-class

platforms and services for an intelligent cloud

and an intelligent edge infused with

artificial intelligence”source: annual report 2017

https://www.sec.gov/Archives/edgar/data/789019/000156459017014900/msft-10k_20170630.htm


source: PwC AI analysis 2017

BIGGEST

OPPORTUNITY$15.7Trillion by 2030 ~ 14% GPD

Productivity gains ($6.6T)

Automation

Increased demand ($9.1T)

Augmentation Higher quality products

http://www.pwc.com/gx/en/issues/analytics/assets/pwc-ai-analysis-sizing-the-prize-report.pdf


source: PwC AI analysis 2017

http://www.pwc.com/gx/en/issues/analytics/assets/pwc-ai-analysis-sizing-the-prize-report.pdf


DNNs (learn complex patterns

on HUGE amounts of data)

Intelligent applications … such as….

Cloud (flexibility, scalability, efficiency)


Speech

recognition

Natural Language

Processing

Computer

Vision


“The more data, The better it works*”

* always read the small print

source: https://xkcd.com/1838/


Best bet to solve intelligence


wait, wait, wait…

What makes deep learning deep?

input hidden output


input hidden hidden hidden hidden output

…

…

…


source: http://www.iro.umontreal.ca/~bengioy/yoshua_en/

“Learning multiple levels of representation to help a learner accomplish a task of interest, with higher levels capturing more abstract concepts through a deeper compositionof computations” Yoshua Bengio


low level features high level featuresmedium level features

Interesting paper about representations: https://arxiv.org/abs/1411.1792


Convolutional Neural Networks

(CNN)

Recurrent Neural Networks

(RNN)


MNIST with CNNs

(source here)

https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_103D_MNIST_ConvolutionalNeuralNetwork.ipynb


source: https://www.cs.toronto.edu/~kriz/cifar.html

CIFAR with ResNet CNN

(source here)

https://github.com/Microsoft/CNTK/blob/master/Examples/Image/Classification/ResNet/Python/TrainResNet_CIFAR10.py


Object detection with FasterRCNN

(source here)

dog

https://github.com/Microsoft/CNTK/tree/master/Examples/Image/Detection/FasterRCNN


Transfer Learning with CNNs

(source here)

https://github.com/Microsoft/CNTK/blob/master/Examples/Image/TransferLearning/TransferLearning.py


Video classification with 3D CNNs

(source here)

https://github.com/Microsoft/CNTK/tree/master/Examples/Video


Text classification with

CNNs (source here)

Input text: “It was a breeze to configure and worked straight away”

Books ElectronicsHome &

KitchenClothing Movies Health Sports

https://blogs.technet.microsoft.com/machinelearning/2017/02/13/cloud-scale-text-classification-with-convolutional-neural-networks-on-microsoft-azure/


Speech recognition

with LSTMs

(source here)

https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_208_Speech_Connectionist_Temporal_Classification.ipynb


Time series forecasting

with LSTMs (source here)

https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_106B_LSTM_Timeseries_with_IOT_Data.ipynb


Autoencoders (source here)

Original LearnedEncoder Decoder

Low dimensional

representation

https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_105_Basic_Autoencoder_for_Dimensionality_Reduction.ipynb


Reinforcement Learning

tutorial (basic & advanced)

source: wikipedia

https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_203_Reinforcement_Learning_Basics.ipynb

https://github.com/Microsoft/CNTK/tree/master/Examples/ReinforcementLearning/FlappingBirdWithKeras


Generative Adversarial Networks (GANs)

tutorial (source here)

https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_206A_Basic_GAN.ipynb


Diagnosis augmentation for lung cancer detection

3D CNN for nodule segmentation +

CNN for nodule classification (info1, info2)

CT scan: horizontal section of lung

Patient health monitoring with sensors (IoT):

Autoencoder on normal class +

reconstruction error abnormal class (info)

https://blogs.technet.microsoft.com/machinelearning/2017/02/17/quick-start-guide-to-the-data-science-bowl-lung-cancer-detection-challenge-using-deep-learning-microsoft-cognitive-toolkit-and-azure-gpu-vms/

https://github.com/lfz/DSB2017

https://arxiv.org/abs/1605.07717


Predictive maintenance with sensors:

LSTM for classification

Visual fault detection in components:

Transfer learning with CNNs


Generative

Asset wealth management and prediction:

Reinforcement Learning for optimal action

policy

Bots for insurance policy management:

LSTM&CNN for speech recognition+

CNN for recommendation system


Route optimization:

Reinforcement Learning

Inventory optimization:

LSTM


Content recommendation:

CNNs featurization + clustering

Personalized marketing:

CNN featurization + clustering


Personalized fashion design and

production:

CNNs for image classification +

GANs for image generation

Customer demand forecasting:

LSTM


Demand forecasting:

LSTM

Predictive maintenance:

LSTM


Supply chain defect reduction

based on images:

Transfer learning with CNNs

On-demand production:

LSTM


source: https://github.com/Azure/fast_retraining

Degradation of accuracy

with new data

Airline delay prediction

You need to retrain!!!

D e e p l e a r n i n g s o l u t i o n s D e e p l e a r n i n g t e c n i q u e s


Learning can be viewed as finding a and b in:

y = a.x + b

y (outputs)x (inputs)

a,b (parameters)


y = a.x + b

1 = a.1 + b

5 = a.5 + b

= a. + b

a=1 b=0

e = ytrue – ypred

ytrue=1ypred depends on a,b

optimize a,b so e isminimum


source: R. Rojas: Neural Networks, Springer-Verlag, Berlin, 1996


Sharpening filter

Laplacian filter

Sobel x-axis filter


Convolution Pooling PoolingConvolutionFully

connected

Fully

connectedInput image Output

predictions

"tabby cat"


Max pooling with 2x2 kernel and stride of 2x2

1 5 3 2

2 6 0 9

1 5 0 1

5 7 2 4

6 9

7 4


input hidden output


SoftmaxReLUtanh


source: Introduction to Natural Language Processing with FastText

Mappings of discrete objects to a high

dimensional space

bird: (1.072, 4.245, …)

dove: (1.134, 4.079, …)

woman: (-1.385, -2.104, …)

man: (-2.207, 2.118, …)

https://github.com/miguelgfierro/sciblog_support/blob/master/Intro_to_NLP_with_fastText/Intro_to_NLP.ipynb


input

hidden

output

hidden

hidden

ti ti+1 ti+2 ti+3

…

…

…


number of layers


source: https://en.wikipedia.org/wiki/Long_short-term_memory


CNTKTensorFlow

https://github.com/ilkarman/DeepLearningFrameworks: 5 frameworks, same problem

https://github.com/ilkarman/DeepLearningFrameworks/


GIVE ME DATA!!!!

CONCEPT DRIFT IS YOUR FRIEND

MASTER THE CORE PRINCIPLES

KNOW THE FRAMEWORKS&TOOLS

Dr. Miguel Fierro

Data Scientist at Microsoft

@miguelgfierrohttps://miguelgfierro.com

Intelligent Cloud GBB Ready | Lisbon Sept 2017

Intelligent Cloud GBB Ready Lisbon 2017

The code from these slides is published at:

https://github.com/ilkarman/DeepLearningFrameworks

Attribution of some images:

• http://morguefile.com

• https://unsplash.com

• Ana Corrales Photography

• http://wikipedia.org

Dataset citation:

• CIFAR-10. Learning Multiple Layers of Features from Tiny

Images, Alex Krizhevsky, 2009.

https://www.cs.toronto.edu/~kriz/cifar.html

• IMDB. https://s3.amazonaws.com/text-datasets/imdb.npz

https://github.com/ilkarman/DeepLearningFrameworks

http://morguefile.com/

https://unsplash.com/

http://wikipedia.org/

https://www.cs.toronto.edu/~kriz/cifar.html

https://s3.amazonaws.com/text-datasets/imdb.npz

Deep Learning for Sales Professionals

Data & Analytics

Transcript of Deep Learning for Sales Professionals