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Deep learning an Introduction with Competitive Landscape
The document discusses various frameworks and architectures within deep learning, highlighting their adoption by prominent organizations such as Google, Microsoft, and Facebook. It also covers the types of neural networks, applications of deep learning, and the hardware and operating systems that support these technologies. Additionally, it outlines challenges in the deep learning ecosystem and notable companies leading in AI advancements.
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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
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- 17. Neural Networks? SoWhat’s New?
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- 20. 22K categories and14Mimages 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 DeepLearning 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 AdoptionOrganization 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 KerasHigh 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 NeonLow-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 VeryHigh. 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
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- 38. OS Adoption Ubuntu (16or 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 MicrosoftCNTK 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.
- 45. Company Product Remarks AlphabetGoogle 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
- 47. Company Product H20.ai SparklingWater 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
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- 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 ofDeep Learning