Page Index - eldakms/CNTK GitHub Wiki

392 page(s) in this GitHub Wiki:

• Home
• The Microsoft Cognitive Toolkit
• Adapt a model I trained on one task to another
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• Articles
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• Associate an id with a prediction
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• Avoid AddSequence Exception
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• Avoid the error CURAND failure 201
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• Baseline Metrics
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• BatchNormalization
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• Binary Operations
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• BrainScript Activation Functions
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• BrainScript and Python Understanding and Extending Readers
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• BrainScript and Python Performance Profiler
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• BrainScript Basic Concepts
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• BrainScript CNTKBinary Reader
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• BrainScript CNTKTextFormat Reader
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• BrainScript Command line parsing rules
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• BrainScript Config file overview
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• BrainScript epochSize and Python epoch_size in CNTK
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• BrainScript expressions
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• BrainScript Full Function Reference
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• BrainScript Functions
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• BrainScript HTKMLF Reader
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• BrainScript Image reader
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• BrainScript Layers Reference
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• BrainScript LM sequence reader
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• BrainScript LU sequence reader
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• BrainScript minibatchSize and Python minibatch_size_in_samples in CNTK
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• BrainScript Model Editing
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• BrainScript Network Builder
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• BrainScript Reader block
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• BrainScript SGD Block
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• BrainScript Top level configurations
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• BrainScript Train, Test, Eval
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• BrainScript UCI Fast Reader
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• Breaking changes in Master compared to beta15
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• Build your own image classifier using Transfer Learning
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• CloneFunction
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• CNTK 1bit SGD License
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• CNTK 2.0 Beta Highlights
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• CNTK 2.0 Python API
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• CNTK 2.0 Setup
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• CNTK 2.0 Setup from Sources
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• CNTK Binary Download and Configuration
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• CNTK Binary Download and Manual Configuration
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• CNTK Docker Containers
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• CNTK Eval Examples
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• CNTK Evaluate Hidden Layers
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• CNTK Evaluate Image Transforms
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• CNTK Evaluate Multiple Models
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• CNTK Evaluation Overview
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• CNTK Evaluation using cntk.exe
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• CNTK FAQ
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• CNTK Library API
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• CNTK Library Evaluation on Linux
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• CNTK Library Evaluation on Windows
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• CNTK Library Evaluation Overview
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• CNTK Library Managed API
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• CNTK Library Native Eval Interface
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• CNTK model format
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• CNTK move to Cuda8
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• CNTK on Azure
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• CNTK Python known issues and limitations
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• CNTK Shared Libraries Naming Format
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• CNTK usage overview
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• CNTK_1_5_Release_Notes
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• CNTK_1_6_Release_Notes
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• CNTK_1_7_1_Release_Notes
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• CNTK_1_7_2_Release_Notes
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• CNTK_1_7_Release_Notes
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• CNTK_2_0_Beta_10_Release_Notes
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• CNTK_2_0_Beta_11_Release_Notes
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• CNTK_2_0_Beta_12_Release_Notes
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• CNTK_2_0_Beta_15_Release_Notes
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• CNTK_2_0_Beta_1_Release_Notes
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• CNTK_2_0_Beta_2_Release_Notes
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• CNTK_2_0_Beta_3_Release_Notes
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• CNTK_2_0_Beta_4_Release_Notes
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• CNTK_2_0_Beta_5_Release_Notes
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• CNTK_2_0_Beta_6_Release_Notes
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• CNTK_2_0_Beta_7_Release_Notes
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• CNTK_2_0_Beta_8_Release_Notes
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• CNTK_2_0_Beta_9_Release_Notes
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• CNTK_2_0_RC_1_Release_Notes
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• Coding Guidelines
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• Compatible dimensions in reader and config
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• Conference Appearances
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• Continue training from a previously saved model
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• Contributing to CNTK
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• ConvertDBN command
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• Convolution
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• Deal with the 'No Output nodes found' error
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• Deal with the error 'No node named 'x'; skipping'
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• Deal with the error 'Reached the maximum number of allowed errors'
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• Debugging CNTK source code in Visual Studio
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• Debugging CNTK's GPU source code in Visual Studio
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• Deep Crossing on CNTK
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• Developing and Testing
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• Do early stopping
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• Dropout
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• Dropout during evaluation
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• Enabling 1bit SGD
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• EvalDll Evaluation on Linux
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• EvalDll Evaluation on Windows
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• EvalDll Evaluation Overview
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• EvalDll Managed API
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• EvalDll Native API
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• Evaluate a model in an Azure WebApi
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• Evaluate my newly trained model but output the activations at an intermediate layer
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• Examples
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• Feedback Channels
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• Gather and Scatter
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• GRUs on CNTK with BrainScript
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• Hands On Labs Image Recognition
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• Hands On Labs Language Understanding
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• How do I
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• How do I Adapt models in Python
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• How do I Deal with Errors in Python
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• How do I Evaluate models in Python
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• How do I Express Things in BrainScript
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• How do I Express Things In Python
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• How do I in BrainScript
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• How do I in Python
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• How do I Read Things in Python
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• How do I run Eval in Azure
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• How do I Train Models in BrainScript
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• How do I Train models in Python
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• How do I use a trained model as a feature extractor
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• How to Test
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• If Operation
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• Image Auto Encoder Using Deconvolution And Unpooling
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• Inputs
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• KDD 2016 Tutorial
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• Layers Library Reference
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• Loss Functions and Metrics
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• Monitor the error on a held out set during training
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• Monitor the error on a held out set during training or do Cross Validation (CV) during training
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• Multiple GPUs and machines
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• News
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• News 2016
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• NuGet Package
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• Object Detection using Fast R CNN
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• OptimizedRNNStack
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• Parameters And Constants
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• Plot command
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• Pooling
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• Post Batch Normalization Statistics
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• Presentations
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• project a 1D input of dim inputDim to a 1D output of dim outputDim
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• Put labels and features in separate files with CNTKTextFormatReader
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• Recommended CNTK 2.0 Setup
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• Records
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• Recurrent Neural Networks with CNTK and applications to the world of ranking
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• Reduction Operations
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• Relate alpha, beta1, beta2 and epsilon to learning rate and momentum in adam_sgd optimizer
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• Sequence to Sequence – Deep Recurrent Neural Networks in CNTK – Part 1
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• Sequence to Sequence – Deep Recurrent Neural Networks in CNTK – Part 2
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• Sequence to Sequence – Deep Recurrent Neural Networks in CNTK – Part 2 – Machine Translation
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• Sequential
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• Setup BuildProtobuf VS15
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• Setup Buildzlib VS15
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• Setup CNTK on Linux
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• Setup CNTK on Windows
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• Setup CNTK on your machine
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• Setup CNTK Python Tools For Windows
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• Setup CNTK with script on Windows
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• Setup Linux Binary Manual
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• Setup Linux Binary Script
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• Setup Linux Python
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• Setup Migrate VS13 to VS15
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• Setup Test Python
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• Setup Windows Binary Manual
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• Setup Windows Binary Script
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• Setup Windows Binary Script Options
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• Setup Windows Devinstall Script Option
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• Setup Windows Python
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• Simple Network Builder
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• Special Nodes
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• Specify multiple label streams with the HTKMLFReader
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• Test Configurations
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• Times and TransposeTimes
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• Top level commands
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• Troubleshoot CNTK
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• Tutorial
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• Tutorial2
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• Tutorials
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• Tutorials, Examples, etc..
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• Unary Operations
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• Update 1bit SGD Submodule Location
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• Use an already trained network multiple times inside a larger network
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• Use built in readers with multiple inputs
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• Using CNTK with BrainScript
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• Using CNTK with multiple GPUs and or machines
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• Using TensorBoard for Visualization
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• Variables
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• Windows Environment Variables
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• WWW 2017 Tutorial
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