Deep learning an Introduction with Competitive Landscape
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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 14M images
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 at Twitter and some universities.
C++ Medium. Common with Hardware vendors and Low lever runtime implementations
Java Very low. Only amongDeeplearning4j users
Slide 2
OS Adoption
Ubuntu (16 or 14) Very Prevalent as a default OS to be supported
Notebooks
Jupyter Almost All examples on JupyterNotebook
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 WatsonPower8 PC with NVLinkHistoric Dominance with Deep Blue
(Chess) and Jeopardy
IBM BlueMixIBM uses Watson to Market itself.
Company Product Remarks
Alphabet Google ML EngineRest API BasedVision APIVideo Intelligence APINatural languageTranslation APIDeep Mind- Solving Artificial General
Intelligence- Impact on Healthcare and Data
Center Power ConsumptionTensor 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 MxnetSimilar Rest based APIas Google
Market Leader in Cloud
Company Product
H20.ai Sparkling Water and Deep Water
SigOpt Improve ML Models
DataRobot Build and Deploy Machine Learning Models
Clarifai.ai Image and Video Tagging
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 as Web 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
