Page Index - wolfma61/CNTK GitHub Wiki
156 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
- Adapt a model I trained on one task to another
- Articles
- Associate an id with a prediction
- Avoid AddSequence Exception
- Avoid the error CURAND failure 201
- Baseline Metrics
- BatchNormalization
- Binary Operations
- BrainScript Network Builder
- BS Basic Concepts
- BS Expressions
- BS Functions
- BS Model Editing
- Build a constant 3D tensor
- CloneFunction
- CNTK 1bit SGD License
- CNTK 2.0 Examples
- CNTK 2.0 Python API
- CNTK 2.0 Setup
- CNTK 2.0 Setup from Sources
- CNTK Binary Download and Configuration
- CNTK Binary Download and Manual Installation
- CNTK Docker Containers
- CNTK Evaluate Hidden Layers
- CNTK Evaluate Image Transforms
- CNTK Evaluate Multiple Models
- CNTK Evaluation Overview
- CNTK FAQ
- CNTK Library API
- CNTK on Azure
- CNTK Python known issues and limitations
- CNTK usage overview
- CNTK_1_5_Release_Notes
- CNTK_1_6_Release_Notes
- CNTK_1_7_1_Release_Notes
- CNTK_1_7_2_Release_Notes
- CNTK_1_7_Release_Notes
- CNTK_2_0_Beta_1_Release_Notes
- CNTKTextFormat Reader
- Coding Guidelines
- Command line parsing rules
- Compatible dimensions in reader and config
- Conference Appearances
- Config file overview
- Continue training from a previously saved model
- Contributing to CNTK
- ConvertDBN command
- Convolution
- Deal with the 'No Output nodes found' error
- Deal with the error 'No node named 'x'; skipping'
- Deal with the error 'Reached the maximum number of allowed errors'
- Debugging CNTK source code in Visual Studio
- Debugging CNTK's GPU source code in Visual Studio
- Deep Crossing on CNTK
- Developing and Testing
- Do early stopping
- Dropout
- Dropout during evaluation
- Enabling 1bit SGD
- Evaluate a model in an Azure WebApi
- Evaluate my newly trained model but output the activations at an intermediate layer
- Examples
- Express a gating mechanism
- Express a softmax over a dynamic axis
- Express a softmax with a temperature parameter
- Express the error rate of my binary classifier
- Full Function Reference
- Gather and Scatter
- Get nice syntax highlighting for BrainScript config files
- Get started in sequence to sequence modelling
- GRUs on CNTK with BrainScript
- Hands On Labs Image Recognition
- Hands On Labs Language Understanding
- How do I
- How do I run Eval in Azure
- How do I use a trained model as a feature extractor
- How to Test
- HTKMLF Reader
- If Operation
- Image reader
- Implement Zoneout
- Inputs
- KDD 2016 Tutorial
- Layer wise training
- Layers Library Reference
- Layers Reference
- LM sequence reader
- Loss Functions and Metrics
- LU sequence reader
- Managed Evaluation Interface
- Monitor the error on a held out set during training
- Monitor the error on a held out set during training or do Cross Validation (CV) during training
- Multiple GPUs and machines
- Native Evaluation Interface
- News
- NuGet Package
- Nuget Package for Evaluation
- Object Detection using Fast R CNN
- OptimizedRNNStack
- Parameters And Constants
- Plot command
- Pooling
- Post Batch Normalization Statistics
- Presentations
- Reader block
- Recommended CNTK 2.0 Setup
- Records
- Recurrent Neural Networks with CNTK and applications to the world of ranking
- Reduction Operations
- Sequence to Sequence – Deep Recurrent Neural Networks in CNTK – Part 1
- Sequence to Sequence – Deep Recurrent Neural Networks in CNTK – Part 2
- Sequence to Sequence – Deep Recurrent Neural Networks in CNTK – Part 2 – Machine Translation
- Sequential
- Setup CNTK on Linux
- Setup CNTK on Windows
- Setup CNTK on your machine
- SGD Block
- Simple Network Builder
- Special Nodes
- Specify multiple label streams with the HTKMLFReader
- Test Configurations
- Times and TransposeTimes
- Top level commands
- Top level configurations
- Train a DSSM (or a convolutional DSSM) model
- Train a multilabel classifier
- Train a regression model on images
- Train with a multitask objective
- Train, Test, Eval
- Troubleshoot CNTK
- Tutorial
- Tutorial2
- Tutorials
- Tutorials, Examples, etc..
- UCI Fast Reader
- Unary Operations
- Understanding and Extending Readers
- Use an already trained network multiple times inside a larger network
- Use built in readers with multiple inputs
- Using CNTK with BrainScript
- Using CNTK with multiple GPUs and or machines
- Variables