Fix typo

Signed-off-by: Paul Dubs <[email protected]>

Author: treo

arbiter-examples/README.md

@@ -1,17 +1,17 @@
 ## Eclipse Deeplearning4j: Arbiter Examples
 
 This project contains a set of examples that demonstrate useage of the Arbiter library for hyperparameter tuning of Deeplearning4J models. More information on Arbiter can be found [here](https://deeplearning4j.konduit.ai/arbiter/overview).
-                                                                                
-[Go back](../README.md) to the main repository page to explore other features/functionality of the **Eclipse DeeplearningJ** ecosystem. File an issue [here](https://github.com/eclipse/deeplearning4j-examples/issues) to request new features.
+
+[Go back](../README.md) to the main repository page to explore other features/functionality of the **Eclipse Deeplearning4J** ecosystem. File an issue [here](https://github.com/eclipse/deeplearning4j-examples/issues) to request new features.
 
 The examples in this project and what they demonstrate are briefly described below. This is also the recommended order to explore them in.
 
 ### Quickstart
-* [BasicHyperparameterOptimizationExample.java](./src/main/java/org/deeplearning4j/arbiterexamples/quickstart/BasicHyperparameterOptimizationExample.java)  
+* [BasicHyperparameterOptimizationExample.java](./src/main/java/org/deeplearning4j/arbiterexamples/quickstart/BasicHyperparameterOptimizationExample.java)
 Conduct random search on two network hyperparameters, and display the search progress in the Arbiter web-based UI.
 
 ### Advanced
-* [BaseGeneticHyperparameterOptimizationExample.java](./src/main/java/org/deeplearning4j/arbiterexamples/advanced/genetic/BaseGeneticHyperparameterOptimizationExample.java)  
+* [BaseGeneticHyperparameterOptimizationExample.java](./src/main/java/org/deeplearning4j/arbiterexamples/advanced/genetic/BaseGeneticHyperparameterOptimizationExample.java)
 Basic hyperparameter optimization example using the genetic candidate generator of Arbiter to conduct a search.
-* [CustomGeneticHyperparameterOptimizationExample.java](./src/main/java/org/deeplearning4j/arbiterexamples/advanced/genetic/CustomGeneticHyperparameterOptimizationExample.java)  
+* [CustomGeneticHyperparameterOptimizationExample.java](./src/main/java/org/deeplearning4j/arbiterexamples/advanced/genetic/CustomGeneticHyperparameterOptimizationExample.java)
 Change the default behavior of the genetic candidate generator.

cuda-specific-examples/README.md

@@ -1,26 +1,26 @@
-## Eclipse Deeplearning4j: CUDA Specific Examples  
+## Eclipse Deeplearning4j: CUDA Specific Examples
 
 Switching from a CPU only backend to a GPU backend is as simple as changing one dependency - one line in the pom.xml file for Maven users. Instead of specifying the nd4j-native-platform module specify the nd4j-cuda-X-platform where X indicated the version of CUDA. It is recommended to install cuDNN for better GPU performance. Runs will log warnings if cuDNN is not found. For more information, please refer to documentation [here](https://deeplearning4j.org/docs/latest/deeplearning4j-config-cudnn)
 
 Users with acces to multiple gpus systems can use DL4J to further speed up the training process by training the models in parallel on them. Ideally these GPUs have the same speed and networking capabilities. This project contains a set of examples that demonstrate how to leverage performance from a multiple gpus setup. More documentation can be found [here](https://deeplearning4j.konduit.ai/getting-started/tutorials/using-multiple-gpus)
 
-[Go back](../README.md) to the main repository page to explore other features/functionality of the **Eclipse DeeplearningJ** ecosystem. File an issue [here](https://github.com/eclipse/deeplearning4j-examples/issues) to request new features.
-        
+[Go back](../README.md) to the main repository page to explore other features/functionality of the **Eclipse Deeplearning4J** ecosystem. File an issue [here](https://github.com/eclipse/deeplearning4j-examples/issues) to request new features.
+
 The list of examples in this project and what they demonstrate are briefly described below. This is also the recommended order to explore them in.
 
 ## QUICKSTART
-* [MultiGPULeNetMNIST.java](./src/main/java/org/deeplearning4j/examples/multigpu/quickstart/MultiGPULeNetMNIST.java)  
+* [MultiGPULeNetMNIST.java](./src/main/java/org/deeplearning4j/examples/multigpu/quickstart/MultiGPULeNetMNIST.java)
 Introduction to ParallelWrapper by modifying the original [LenetMnistExample](./../dl4j-examples/src/main/java/org/deeplearning4j/examples/quickstart/modeling/convolution/LeNetMNIST.java)
-* [GradientsSharingLeNetMNIST.java](./src/main/java/org/deeplearning4j/examples/multigpu/quickstart/GradientsSharingLeNetMNIST.java)  
+* [GradientsSharingLeNetMNIST.java](./src/main/java/org/deeplearning4j/examples/multigpu/quickstart/GradientsSharingLeNetMNIST.java)
 Uses gradient sharing instead of the default averaging every n iterations. More information on the gradient sharing algorithm can be found [here](https://deeplearning4j.konduit.ai/distributed-deep-learning/intro)
-* [GradientSharingVGG16TinyImageNet.java](./src/main/java/org/deeplearning4j/examples/multigpu/quickstart/GradientSharingVGG16TinyImageNet.java)  
+* [GradientSharingVGG16TinyImageNet.java](./src/main/java/org/deeplearning4j/examples/multigpu/quickstart/GradientSharingVGG16TinyImageNet.java)
 Gradient sharing with VGG16 on TinyImageNet
 
 ## ADVANCED
-* [ImdbReviewClassificationRNN.java](./src/main/java/org/deeplearning4j/examples/multigpu/advanced/w2vsentiment/ImdbReviewClassificationRNN.java)  
+* [ImdbReviewClassificationRNN.java](./src/main/java/org/deeplearning4j/examples/multigpu/advanced/w2vsentiment/ImdbReviewClassificationRNN.java)
 A multiple gpus version of the example of the same name in the dl4j-examples repo [here](./../dl4j-examples//src/main/java/org/deeplearning4j/examples/advanced/modelling/textclassification/pretrainedword2vec/ImdbReviewClassificationRNN.java) This example also includes how to presave the dataset to save time when training on multiple epochs.
-* [GenerateTxtModel.java](./src/main/java/org/deeplearning4j/examples/multigpu/advanced/charmodelling/GenerateTxtModel.java)  
+* [GenerateTxtModel.java](./src/main/java/org/deeplearning4j/examples/multigpu/advanced/charmodelling/GenerateTxtModel.java)
 CharModelling: A multiple gpus version of the example of the same name in the dl4j-examples repo, [here](./../dl4j-examples/src/main/java/org/deeplearning4j/examples/advanced/modelling/charmodelling/generatetext/GenerateTxtModel.java).
-* [FeaturizedPreSave.java](./src/main/java/org/deeplearning4j/examples/multigpu/advanced/transferlearning/vgg16/FeaturizedPreSave.java) & [FitFromFeaturized.java](./src/main/java/org/deeplearning4j/examples/multigpu/advanced/transferlearning/vgg16/FitFromFeaturized.java)  
+* [FeaturizedPreSave.java](./src/main/java/org/deeplearning4j/examples/multigpu/advanced/transferlearning/vgg16/FeaturizedPreSave.java) & [FitFromFeaturized.java](./src/main/java/org/deeplearning4j/examples/multigpu/advanced/transferlearning/vgg16/FitFromFeaturized.java)
 Transferlearning: A multiple gpus version of the example of the same name in the dl4j-examples repo [here](./../dl4j-examples/src/main/java/org/deeplearning4j/examples/advanced/features/transferlearning/editlastlayer/presave)
 

data-pipeline-examples/README.md

@@ -1,55 +1,55 @@
 ## Eclipse Deeplearning4j: Data pipeline, DataVec Examples
 
 This project contains a set of examples that demonstrate how raw data in various formats can be loaded, split and preprocessed to build serializable (and hence reproducible) ETL pipelines using the DataVec library.
-                                                                                
-[Go back](../README.md) to the main repository page to explore other features/functionality of the **Eclipse DeeplearningJ** ecosystem. File an issue [here](https://github.com/eclipse/deeplearning4j-examples/issues) to request new features.
-        
+
+[Go back](../README.md) to the main repository page to explore other features/functionality of the **Eclipse Deeplearning4J** ecosystem. File an issue [here](https://github.com/eclipse/deeplearning4j-examples/issues) to request new features.
+
 The examples in this project and what they demonstrate are briefly described below. This is also the recommended order to explore them in.
 
 ### Loading Data
-InputSplit and its implementations are utility classes for defining and managing a catalog of loadable locations (paths/files), in memory, that can later be exposed through an Iterator. In simple terms, they define where your data comes from or should be saved to, when building a data pipeline with DataVec.  
+InputSplit and its implementations are utility classes for defining and managing a catalog of loadable locations (paths/files), in memory, that can later be exposed through an Iterator. In simple terms, they define where your data comes from or should be saved to, when building a data pipeline with DataVec.
 
-* [Ex01_FileSplitExample.java](./src/main/java/org/deeplearning4j/datapipelineexamples/loading/Ex01_FileSplitExample.java)  
+* [Ex01_FileSplitExample.java](./src/main/java/org/deeplearning4j/datapipelineexamples/loading/Ex01_FileSplitExample.java)
 Using FileSplit which loads files in a given location. Constructor overloading allows for varying functionality like filtering files to load, loading recursively etc
-* [Ex02_CollectionSplitExample.java](./src/main/java/org/deeplearning4j/datapipelineexamples/loading/Ex02_CollectionSplitExample.java)  
+* [Ex02_CollectionSplitExample.java](./src/main/java/org/deeplearning4j/datapipelineexamples/loading/Ex02_CollectionSplitExample.java)
 Create a split from a collection of URIs
-* [Ex03_NumberedFileInputSplitExample.java](./src/main/java/org/deeplearning4j/datapipelineexamples/loading/Ex03_NumberedFileInputSplitExample.java)  
+* [Ex03_NumberedFileInputSplitExample.java](./src/main/java/org/deeplearning4j/datapipelineexamples/loading/Ex03_NumberedFileInputSplitExample.java)
 Create a split from numbered files, following a common pattern like file1.txt, file2.txt ... file100.txt
-* [Ex04_TransformSplitExample.java](./src/main/java/org/deeplearning4j/datapipelineexamples/loading/Ex04_TransformSplitExample.java)  
+* [Ex04_TransformSplitExample.java](./src/main/java/org/deeplearning4j/datapipelineexamples/loading/Ex04_TransformSplitExample.java)
 Maps URIs of a given split to new URIs. Useful when features and labels are in different files sharing a common naming scheme, and the name of the output file can be determined given the name of the input file. Eg. a-in.csv and a-out.csv
-* [Ex05_SamplingBaseInputSplitExample.java](./src/main/java/org/deeplearning4j/datapipelineexamples/loading/Ex05_SamplingBaseInputSplitExample.java)  
+* [Ex05_SamplingBaseInputSplitExample.java](./src/main/java/org/deeplearning4j/datapipelineexamples/loading/Ex05_SamplingBaseInputSplitExample.java)
 Generate several splits from the main split say for training, validation and testing.
-* [Ex06_KFoldIteratorFromDataSet.java](./src/main/java/org/deeplearning4j/datapipelineexamples/loading/Ex06_KFoldIteratorFromDataSet.java)  
+* [Ex06_KFoldIteratorFromDataSet.java](./src/main/java/org/deeplearning4j/datapipelineexamples/loading/Ex06_KFoldIteratorFromDataSet.java)
 Generate a K-Fold iterator from a dataset
 
 ### Cleaning, Transforming and Analysing Data
-* [IrisCSVTransform.java](./src/main/java/org/deeplearning4j/datapipelineexamples/transform/basic/IrisCSVTransform.java)  
+* [IrisCSVTransform.java](./src/main/java/org/deeplearning4j/datapipelineexamples/transform/basic/IrisCSVTransform.java)
 A basic example that introduces users to important concepts like Schema and TransformProcess with categoricalToInteger.
-* [CSVMixedDataTypesLocal.java](./src/main/java/org/deeplearning4j/datapipelineexamples/transform/basic/CSVMixedDataTypesLocal.java)  
+* [CSVMixedDataTypesLocal.java](./src/main/java/org/deeplearning4j/datapipelineexamples/transform/basic/CSVMixedDataTypesLocal.java)
 Common preprocessing steps like removing unnecessary columns, filtering based on column value, replacing invalid values, parsing date time etc
-* [CSVMixedDataTypes.java](./src/main/java/org/deeplearning4j/datapipelineexamples/transform/basic/CSVMixedDataTypes.java)  
+* [CSVMixedDataTypes.java](./src/main/java/org/deeplearning4j/datapipelineexamples/transform/basic/CSVMixedDataTypes.java)
 Same as the above but with Apache Spark
-* [PrintSchemasAtEachStep.java](./src/main/java/org/deeplearning4j/datapipelineexamples/transform/debugging/PrintSchemasAtEachStep.java)  
+* [PrintSchemasAtEachStep.java](./src/main/java/org/deeplearning4j/datapipelineexamples/transform/debugging/PrintSchemasAtEachStep.java)
 How to print schema at each step which would be useful for debugging transform scripts in a complicated pipeline
-* [IrisAnalysis.java](./src/main/java/org/deeplearning4j/datapipelineexamples/analysis/IrisAnalysis.java)  
+* [IrisAnalysis.java](./src/main/java/org/deeplearning4j/datapipelineexamples/analysis/IrisAnalysis.java)
 Basic Analysis of the dataset saved and presented as an html file
-* [IrisNormalizer.java](./src/main/java/org/deeplearning4j/datapipelineexamples/transform/basic/IrisNormalizer.java)  
+* [IrisNormalizer.java](./src/main/java/org/deeplearning4j/datapipelineexamples/transform/basic/IrisNormalizer.java)
 Proper useage of preprocessors with min max scaler
-* [JoinExample.java](./src/main/java/org/deeplearning4j/datapipelineexamples/transform/basic/JoinExample.java)  
+* [JoinExample.java](./src/main/java/org/deeplearning4j/datapipelineexamples/transform/basic/JoinExample.java)
 Perform joins on datasets
-* [PivotExample.java](./src/main/java/org/deeplearning4j/datapipelineexamples/transform/basic/PivotExample.java)  
-Combine multiple independent records by key. 
-* [WebLogDataExample.java](./src/main/java/org/deeplearning4j/datapipelineexamples/transform/basic/WebLogDataExample.java)  
+* [PivotExample.java](./src/main/java/org/deeplearning4j/datapipelineexamples/transform/basic/PivotExample.java)
+Combine multiple independent records by key.
+* [WebLogDataExample.java](./src/main/java/org/deeplearning4j/datapipelineexamples/transform/basic/WebLogDataExample.java)
 Preprocessing/aggregation operations on some web log data
-* [CustomReduceExample.java](./src/main/java/org/deeplearning4j/datapipelineexamples/transform/custom/CustomReduceExample.java)  
+* [CustomReduceExample.java](./src/main/java/org/deeplearning4j/datapipelineexamples/transform/custom/CustomReduceExample.java)
 Custom Reduction example for operations on some simple CSV data that involve a custom reduction.
-* [MultiOpReduceExample.java](./src/main/java/org/deeplearning4j/datapipelineexamples/transform/custom/MultiOpReduceExample.java)  
+* [MultiOpReduceExample.java](./src/main/java/org/deeplearning4j/datapipelineexamples/transform/custom/MultiOpReduceExample.java)
 Reduce example with multiple ops on one column
 
 ### Formats
-* [CSVtoMapFileConversion.java](./src/main/java/org/deeplearning4j/datapipelineexamples/formats/hdfs/conversion/CSVtoMapFileConversion.java)  
+* [CSVtoMapFileConversion.java](./src/main/java/org/deeplearning4j/datapipelineexamples/formats/hdfs/conversion/CSVtoMapFileConversion.java)
 A simple example on how to convert a CSV text file to a Hadoop MapFile format for better performance and the convenience of randomization supported by the MapFileRecordReader
-* [SVMLightExample.java](./src/main/java/org/deeplearning4j/datapipelineexamples/formats/svmlight/SVMLightExample.java)  
+* [SVMLightExample.java](./src/main/java/org/deeplearning4j/datapipelineexamples/formats/svmlight/SVMLightExample.java)
 MNIST SVMLight example
-* [ImagePipelineExample.java](./src/main/java/org/deeplearning4j/datapipelineexamples/formats/image/ImagePipelineExample.java)  
+* [ImagePipelineExample.java](./src/main/java/org/deeplearning4j/datapipelineexamples/formats/image/ImagePipelineExample.java)
 An imagepipeline that also demonstrates using transforms to augment a small dataset

dl4j-distributed-training-examples/README.md

@@ -1,17 +1,17 @@
 ## Eclipse Deeplearning4j: Distributed Training Examples
 
-This project contains a set of examples that demonstrate how to do distributed training in DL4J. DL4J distributed training employs a "hybrid" asynchronous SGD based on Niko Strom's paper linked [here](http://nikkostrom.com/publications/interspeech2015/strom_interspeech2015.pdf). More information on DL4J's distributed training methods and how they work can be found [here](https://deeplearning4j.konduit.ai/distributed-deep-learning/intro). DL4J's distributed training implementation is also fault tolerant. 
+This project contains a set of examples that demonstrate how to do distributed training in DL4J. DL4J distributed training employs a "hybrid" asynchronous SGD based on Niko Strom's paper linked [here](http://nikkostrom.com/publications/interspeech2015/strom_interspeech2015.pdf). More information on DL4J's distributed training methods and how they work can be found [here](https://deeplearning4j.konduit.ai/distributed-deep-learning/intro). DL4J's distributed training implementation is also fault tolerant.
 
-Of note - Spark is only relied upon for three specific tasks: 1) Broadcasting the initial neural network parameters to all workers 2) Distributing the RDD datasets to the workers 3) Spark's fault tolerance system to detect and bring up a replacement workers. For all other communication between nodes like transferring quantized gradient updates Aeron is used.  
+Of note - Spark is only relied upon for three specific tasks: 1) Broadcasting the initial neural network parameters to all workers 2) Distributing the RDD datasets to the workers 3) Spark's fault tolerance system to detect and bring up a replacement workers. For all other communication between nodes like transferring quantized gradient updates Aeron is used.
+
+[Go back](../README.md) to the main repository page to explore other features/functionality of the **Eclipse Deeplearning4J** ecosystem. File an issue [here](https://github.com/eclipse/deeplearning4j-examples/issues) to request new features.
 
-[Go back](../README.md) to the main repository page to explore other features/functionality of the **Eclipse DeeplearningJ** ecosystem. File an issue [here](https://github.com/eclipse/deeplearning4j-examples/issues) to request new features.
-        
 The examples in this project and what they demonstrate are briefly described below. This is also the recommended order to explore them in.
 
-* [tinyimagenet](src/main/java/org/deeplearning4j/distributedtrainingexamples/tinyimagenet)  
+* [tinyimagenet](src/main/java/org/deeplearning4j/distributedtrainingexamples/tinyimagenet)
 Train a CNN network from scratch on the Tiny ImageNet dataset. A local (single machine) version is also available.
 
 * [Patent Classification](src/main/java/org/deeplearning4j/distributedtrainingexamples/patent/README.md)
 A real world document classification example on ~500GB of raw text. A local (single machine) version is also provided to demonstrate the reduction in training time to converge to the same level of the accuracy. Experiments have demonstrated a near linear scaling with the number of workers in the cluster!
 
-NOTE: For parallel inference take a look at the 
+NOTE: For parallel inference take a look at the