Page Index - wolfma61/CNTK GitHub Wiki
299 page(s) in this GitHub Wiki:
- Home
- CNTK
- What's New
- October 2016
- September 2016
- August 2016
- July 2016
- June 2016
- May 2016
- April 2016
- March 2016
- February 2016
- January 2016
- Activation Functions
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- 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 Network Builder
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- BS Basic Concepts
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- BS Expressions
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- BS Functions
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- BS Model Editing
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- Build a constant 3D tensor
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- CloneFunction
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- CNTK 1bit SGD License
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- CNTK 2.0 Examples
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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 Installation
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- CNTK Docker Containers
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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 FAQ
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- CNTK Library API
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- CNTK on Azure
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- CNTK Python known issues and limitations
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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_1_Release_Notes
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- CNTKTextFormat Reader
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- Coding Guidelines
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- Command line parsing rules
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- Compatible dimensions in reader and config
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- Conference Appearances
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- Config file overview
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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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- 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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- Express a gating mechanism
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- Express a softmax over a dynamic axis
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- Express a softmax with a temperature parameter
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- Express the error rate of my binary classifier
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- Full Function Reference
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- Gather and Scatter
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- Get nice syntax highlighting for BrainScript config files
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- Get started in sequence to sequence modelling
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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 run Eval in Azure
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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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- HTKMLF Reader
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- If Operation
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- Image reader
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- Implement Zoneout
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- Inputs
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- KDD 2016 Tutorial
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- Layer wise training
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- Layers Library Reference
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- Layers Reference
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- LM sequence reader
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- Loss Functions and Metrics
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- LU sequence reader
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- Managed Evaluation Interface
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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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- Native Evaluation Interface
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- News
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- NuGet Package
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- Nuget Package for Evaluation
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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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- Reader block
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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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- 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 CNTK on Linux
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- Setup CNTK on Windows
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- Setup CNTK on your machine
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- SGD Block
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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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- Top level configurations
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- Train a DSSM (or a convolutional DSSM) model
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- Train a multilabel classifier
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- Train a regression model on images
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- Train with a multitask objective
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- Train, Test, Eval
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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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- UCI Fast Reader
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- Unary Operations
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- Understanding and Extending Readers
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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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- Variables
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