GitBook: [master] 8 pages modified

Author: adamltyson

SUMMARY.md

@@ -21,8 +21,8 @@
   * [Cell candidate detection](user-guide/usage/cell-candidate-detection.md)
   * [Cell candidate classification](user-guide/usage/cell-candidate-classification.md)
   * [Historical options](user-guide/usage/historical-options.md)
-* [Training the network](user-guide/untitled-1/README.md)
-  * [Using supplied training data](user-guide/untitled-1/using-supplied-training-data.md)
+* [Training the network](user-guide/training/README.md)
+  * [Using supplied training data](user-guide/training/using-supplied-training-data.md)
 * [Visualisation](user-guide/visualisation.md)
 * [Group-level analysis](user-guide/group-level-analysis/README.md)
   * [Summarising multiple cell counts](user-guide/group-level-analysis/untitled-2.md)

installation/setting-up-your-gpu.md

@@ -6,7 +6,7 @@ description: How to speed up cellfinder by using your GPU
 
 ## Introduction
 
-**cellfinder** will run quite happily on your CPU, but the machine learning parts \(classifying cell candidates as cells or artefacts, and [Training the network](../user-guide/untitled-1/)\) **run much faster** using  GPU.
+**cellfinder** will run quite happily on your CPU, but the machine learning parts \(classifying cell candidates as cells or artefacts, and [Training the network](../user-guide/training/)\) **run much faster** using  GPU.
 
 #### Requirements
 

user-guide/data-requirements.md

@@ -12,7 +12,7 @@ cellfinder was written to analyse certain kinds of whole brain microscopy datase
 
 For registration, you only need a single channel, but this is ideally a "background" channel, i.e. one with only autofluroescence, and no other strong signal. Typically we acquire the "signal" channels with red or green filters, and then the "background" channel with blue filters.
 
-For cell detection, you will need two channels, the "signal" channel, and the "background" channel. The signal channel should contain brightly labelled cells \(e.g. from staining or viral injections\). The models supplied with cellfinder were trained on whole-cell labels, so if you have e.g. a nuclear marker, they will need to be retrained \(see [Training the network](untitled-1/)\). However, realistically, the network will need to be retrained for every new application
+For cell detection, you will need two channels, the "signal" channel, and the "background" channel. The signal channel should contain brightly labelled cells \(e.g. from staining or viral injections\). The models supplied with cellfinder were trained on whole-cell labels, so if you have e.g. a nuclear marker, they will need to be retrained \(see [Training the network](training/)\). However, realistically, the network will need to be retrained for every new application
 
 ### Image structure
 

user-guide/getting-started.md

@@ -37,5 +37,5 @@ If you have any spaces in your file-path, please enclose it in quotation marks,
 
 ### Retraining the machine learning network to classify cells
 
-The deep learning network included with cellfinder to classify cells as real cells or artefacts was trained on a very specific dataset. You will very likely need to retrain this if the classification is incorrect on your data. See [Training the network](untitled-1/).
+The deep learning network included with cellfinder to classify cells as real cells or artefacts was trained on a very specific dataset. You will very likely need to retrain this if the classification is incorrect on your data. See [Training the network](training/).