Brief Introduction about the technology - Deep Learning & Microsoft Cognitive ToolKit (CNTK).
by Marvin, Heng
Twitter: @hmheng
Blog: www.techconnect.io
#artificialintelligence #ai #MachineLearning
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AI/ML/DL: Introduction to Deep Learning with Cognitive ToolKit
1. Introduction to
Deep Learning with
Cognitive Toolkit (CNTK)
Marvin Heng
Blog : http://hmheng.pinsland.com
Twitter : @hmheng
YouTube: http://bit.ly/hmheng_yt
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Github: https://github.com/hmheng
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Microsoft
Cognitive Service
Microsoft
Cognitive ToolKit
2. Who is Marvin?
Software + Web Developer
AI Enthusiast
Blog : https://hmheng.pinsland.com
Twitter : @hmheng
YouTube: http://bit.ly/hmheng_yt
SlideShare: http://bit.ly/hmheng_ss
Github: https://github.com/hmheng
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5. What is Deep Learning?
A Subfield of Machine Learning
Self Learning
More complex than Machine Learning
Neural Networks mimics the neural systems in our brains.
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6. What is Deep Learning?
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Credit: Nvidia
9. Microsoft Cognitive Toolkit (CNTK)?
• Open-Source Development on GitHub (released 2.5 on 15th Mar 18)
• Fast
• Optimized for GPUs & Libraries
• Best-in-Class multi-GPU/Multi-server algorithms
• Flexible
• BrainScript, C#/.NET, C++, Python, Network Definition Language, Model Editing Language(MEL)
• Linux, Windows and Docker Container
• Ease of Use & Wide-range of Support
• FF-NN, CNN, RNN, LSTM, DSSM,…
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10. Microsoft Cognitive Toolkit (CNTK)
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Arbitrary Neural
Networks Expression
Composing simple building
blocks into complex
computational networks.
Production
Ready
State-of-the-art accuracy
Efficient
Scalable to Multi-GPU/Multi-
Server
11. Microsoft Cognitive Toolkit
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Design
Number of layers,
number of units
Dependent on
concept
complexity
Experimentation
often required
Activation
Function
Enables non-
linear models
Examples include
sigmoid,
hyperbolic
tangent, ReLU,
softmax, …
Loss
Function
Measure the
performance
during training
Examples include
cross entropy
Training
Mechanism for
getting optimal
weights
Overfitting
Stop models from
over-optimizing
on training data
set.
12. Microsoft Cognitive Toolkit (CNTK)
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Windows
CPU|GPU
NuGet – CPU | NuGet – GPU | NuGet – UWP CPU
Linux
CPU|GPU|Docker – CPU| Docker – GPU
Requires OpenMPI
MacOS
Docker – CPU
Cloud
Data Science Virtual Machine | Azure Notebooks
Azure Batch AI Training (CPU/GPU) | Project BrainWave (FPGA)
13. Sharing
Marvin Heng
Blog : http://hmheng.pinsland.com
Twitter : @hmheng
YouTube: http://bit.ly/hmheng_yt
SlideShare: http://bit.ly/hmheng_ss
Github: https://github.com/hmheng
Microsoft
Cognitive Service
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Microsoft
Cognitive ToolKit
15. Open Neural Network Exchange
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• An open format to represent deep learning models.
• Collaboration between Microsoft, AWS and Facebook.
• Supported by community of partners like Intel, ARM, AMD, nvidia,
etc.
• A share library with Caffe2, PyTorch, MXNet, Cognitive Toolkit.
• Permissive MIT license and no patents.
16. ONNX Aims
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• Allow interoperability between frameworks
• CNTK, Caffe2 and PyTorch
• Allow hardware vendor to focus on one IR in their backend
optimization
• Allow training in one toolkit and deploy in another.
17. ONNX Aims
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• Allow interoperability between frameworks
• CNTK, Caffe2 and PyTorch
• Allow hardware vendor to focus on one IR in their backend
optimization
• Allow training in one toolkit and deploy in another.
19. Marvin Heng
Blog : http://hmheng.pinsland.com
Twitter : @hmheng
YouTube: http://bit.ly/hmheng_yt
SlideShare: http://bit.ly/hmheng_ss
Github: https://github.com/hmheng
Thank
You
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20. Introduction to
Deep Learning with
Cognitive Toolkit (CNTK)
Marvin Heng
Blog : http://hmheng.pinsland.com
Twitter : @hmheng
YouTube: http://bit.ly/hmheng_yt
SlideShare: http://bit.ly/hmheng_ss
Github: https://github.com/hmheng
HMHENG.
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Microsoft
Cognitive Service
Microsoft
Cognitive ToolKit