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1  sur  56
aurius.io
???
Sigmoid RELU
tanh Leaky RELU
http://neuralnetworksanddeeplearning.com/chap1.html)
“2-layer Neural Net”, or
“1-hidden-layer Neural Net”
“3-layer Neural Net”, or
“2-hidden-layer Neural Net”
“Fully-connected” layers
GoogleNet
AlexNet
ResNet
Neural Networks? So What’s
New?
Deep Learning Disruption
22K categories and 14Mimages
www.image-net.org
Deng, Dong, Socher, Li, Li, & Fei-Fei, 2009
22Fei-Fei Li & Justin Johnson & Serena Yeung
• Animals
• Bird
• Fish
• Mammal
• Invertebrate
• Plants
•Tree
• Flower
Food
Materials
Structures
Artifact
• Tools
• Appliances
• Structures
•
•
•
Person
Scenes
• Indoor
• Geological
Formations
SportActivities
Types of Deep Learning
Networks
Convolution Neural Network
Architecture
pixels
edges
object parts
(combination
of edges)
object models
• Need Context
• Images do not carry context
• Languages – Complex
• ”I like spicy food, but it makes
me uncomfortable”?
Frameworks
Framework Name Adoption Organization
Tensorflow High Google
Caffe/Caffe2 Medium-High Facebook, UC Berkeley
(Good support for Image
analysis)- Caffe2 released in
2017
Mxnet Low Amazon.
Released in 2017
CNTK Medium (High in Microsoft
Users)
Microsoft.
Good example with Image
Identification (COCO
dataset)
Theano Medium University of Montreal. One
of the oldests frameworks.
Framework Adoption Organization
Keras High Google. Extremely popular.
Torch/PyTorch Medium - High Open Source.Twitter uses it.
Very popular in Non Python
user base
DeepLearning4J Medium DeepLearning4J. Small
company in SF, started in
2014. Good Java and Hadoop
support. Loosing grounds to
Tensorflow.
Chainer Low-Medium Preferred Networks. A
japanese company.
Applications in IOT and
Robotics
Framework Adoption Organization
Neon Low-Medium Intel. Nervana acquired in
2016. Fastest DL Framework
BigDL Low Intel. Support for running
DeepLearning on Spark.
Python Numpy like API. Built
in support for Intel MKL
libraries. Cloudera Supports
CUDA High Nvidia. All frameworks use it
and Self Driving Car industry
TensorRT Low Nvidia. Optimizes the Deep
Learning layers, increasing
inference performance.
Language Adoption
Python Very High. Most Common.Works
well with numpy, openCV, scikit-
learn.
Lua (Torch) Medium. Used atTwitter and some
universities.
C++ Medium. Common with Hardware
vendors and Low lever runtime
implementations
Java Very low. Only among
Deeplearning4j users
Slide 2
OS Adoption
Ubuntu (16 or 14) Very Prevalent as a default OS to
be supported
Notebooks
Jupyter Almost All examples on Jupyter
Notebook
Hardware
Deep Learning Ecosystem
Google > 50% Mindshare of the AI Market
Company Product Remarks
Microsoft CNTK
https://studio.azureml.net/
- A very comprehensive support for
Machine Learning Libraries.
- A well designed Interface
Azure Cloud is growing very fast.
They have actively taken up market
share from Amazon
IBM Watson
Power8 PC with NVLink
Historic Dominance with Deep Blue
(Chess) and Jeopardy
IBM BlueMix
IBM uses Watson to Market itself.
Company Product Remarks
Alphabet Google ML Engine
Rest API Based
Vision API
Video Intelligence API
Natural language
Translation API
Deep Mind
- Solving Artificial General
Intelligence
- Impact on Healthcare and Data
Center Power Consumption
Tensor Processing Unit
- Competing with Nvida
- Will be offered as a Cloud Service
Company with largest Mindshare in
Artificial Intelligence.
I think Google will be the biggest
competitor in the Cloud Business
going forward.
https://cloud.google.com/products/
Amazon - Apache Mxnet
Similar Rest based API
as Google
Market Leader in Cloud
Company Product
H20.ai Sparkling Water and DeepWater
SigOpt Improve ML Models
DataRobot Build and Deploy Machine Learning Models
Clarifai.ai Image andVideoTagging
Crowdflower.ai Dataset preparation for Uber and many
companies
Clarifai.ai
Sample Machine Learning – Life Cycle
Get/Prepare
Data
Build/Edit
Experiment
Create/Update
Model
Evaluate
Model
Results
Build ML Model
Deploy asWeb Service
Provision Environment
Create Cluster
Publish an App
Integrate with
App/Analytics
Publish the
model
Deploy Model as a
Web Service
Examine the Predictions / Use
more production data to fine tune
Model
Challenges
Who’s Who of Deep Learning
Deep learning an Introduction with Competitive Landscape
Deep learning an Introduction with Competitive Landscape
Deep learning an Introduction with Competitive Landscape
Deep learning an Introduction with Competitive Landscape

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Deep learning an Introduction with Competitive Landscape

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  • 15. “2-layer Neural Net”, or “1-hidden-layer Neural Net” “3-layer Neural Net”, or “2-hidden-layer Neural Net” “Fully-connected” layers
  • 17. Neural Networks? So What’s New?
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  • 20. 22K categories and 14Mimages www.image-net.org Deng, Dong, Socher, Li, Li, & Fei-Fei, 2009 22Fei-Fei Li & Justin Johnson & Serena Yeung • Animals • Bird • Fish • Mammal • Invertebrate • Plants •Tree • Flower Food Materials Structures Artifact • Tools • Appliances • Structures • • • Person Scenes • Indoor • Geological Formations SportActivities
  • 21. Types of Deep Learning Networks
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  • 26. • Need Context • Images do not carry context • Languages – Complex • ”I like spicy food, but it makes me uncomfortable”?
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  • 33. Framework Name Adoption Organization Tensorflow High Google Caffe/Caffe2 Medium-High Facebook, UC Berkeley (Good support for Image analysis)- Caffe2 released in 2017 Mxnet Low Amazon. Released in 2017 CNTK Medium (High in Microsoft Users) Microsoft. Good example with Image Identification (COCO dataset) Theano Medium University of Montreal. One of the oldests frameworks.
  • 34. Framework Adoption Organization Keras High Google. Extremely popular. Torch/PyTorch Medium - High Open Source.Twitter uses it. Very popular in Non Python user base DeepLearning4J Medium DeepLearning4J. Small company in SF, started in 2014. Good Java and Hadoop support. Loosing grounds to Tensorflow. Chainer Low-Medium Preferred Networks. A japanese company. Applications in IOT and Robotics
  • 35. Framework Adoption Organization Neon Low-Medium Intel. Nervana acquired in 2016. Fastest DL Framework BigDL Low Intel. Support for running DeepLearning on Spark. Python Numpy like API. Built in support for Intel MKL libraries. Cloudera Supports CUDA High Nvidia. All frameworks use it and Self Driving Car industry TensorRT Low Nvidia. Optimizes the Deep Learning layers, increasing inference performance.
  • 36. Language Adoption Python Very High. Most Common.Works well with numpy, openCV, scikit- learn. Lua (Torch) Medium. Used atTwitter and some universities. C++ Medium. Common with Hardware vendors and Low lever runtime implementations Java Very low. Only among Deeplearning4j users
  • 38. OS Adoption Ubuntu (16 or 14) Very Prevalent as a default OS to be supported Notebooks Jupyter Almost All examples on Jupyter Notebook
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  • 42. Google > 50% Mindshare of the AI Market
  • 43. Company Product Remarks Microsoft CNTK https://studio.azureml.net/ - A very comprehensive support for Machine Learning Libraries. - A well designed Interface Azure Cloud is growing very fast. They have actively taken up market share from Amazon IBM Watson Power8 PC with NVLink Historic Dominance with Deep Blue (Chess) and Jeopardy IBM BlueMix IBM uses Watson to Market itself.
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  • 45. Company Product Remarks Alphabet Google ML Engine Rest API Based Vision API Video Intelligence API Natural language Translation API Deep Mind - Solving Artificial General Intelligence - Impact on Healthcare and Data Center Power Consumption Tensor Processing Unit - Competing with Nvida - Will be offered as a Cloud Service Company with largest Mindshare in Artificial Intelligence. I think Google will be the biggest competitor in the Cloud Business going forward. https://cloud.google.com/products/ Amazon - Apache Mxnet Similar Rest based API as Google Market Leader in Cloud
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  • 47. Company Product H20.ai Sparkling Water and DeepWater SigOpt Improve ML Models DataRobot Build and Deploy Machine Learning Models Clarifai.ai Image andVideoTagging Crowdflower.ai Dataset preparation for Uber and many companies
  • 49. Sample Machine Learning – Life Cycle Get/Prepare Data Build/Edit Experiment Create/Update Model Evaluate Model Results Build ML Model Deploy asWeb Service Provision Environment Create Cluster Publish an App Integrate with App/Analytics Publish the model Deploy Model as a Web Service Examine the Predictions / Use more production data to fine tune Model
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  • 52. Who’s Who of Deep Learning