Update docs

Author: surban

.vscode/settings.json

@@ -12,5 +12,13 @@
     "FSharp.keywordsAutocomplete": false,
     "FSharp.minimizeBackgroundParsing": true,
     "FSharp.resolveNamespaces": false,
+    "cSpell.words": [
+        "BLAS",
+        "Bezout's",
+        "CUDA",
+        "MATLAB",
+        "SIMD",
+        "nVidia"
+    ],
 
 }

Tensor/README.md

@@ -7,7 +7,7 @@ A *tensor* is an n-dimensional array of an arbitrary data type (for example `sin
 Tensors of data type `'T` are implemented by the [Tensor<'T>](xref:Tensor.Tensor`1) type.
 
 A tensor can be either stored in host memory or in the memory of a GPU computing device.
-Currenty only nVidia cards implementing the [CUDA API](https://developer.nvidia.com/cuda-zone) are supported.
+Currently only nVidia cards implementing the [CUDA API](https://developer.nvidia.com/cuda-zone) are supported.
 The API for host and GPU stored tensors is mostly equal, thus a program can make use of GPU accelerated operations without porting effort.
 
 The tensor library provides functionality similar to [Numpy's Ndarray](http://docs.scipy.org/doc/numpy-1.10.0/reference/arrays.html) and [MATLAB arrays](http://www.mathworks.com/help/matlab/matrices-and-arrays.html), including vector-wise operations, reshaping, slicing, broadcasting, masked assignment, reduction operations and BLAS operations.
@@ -17,12 +17,12 @@ This open source library is written in [F#](http://fsharp.org/) and targets the
 ### Features provided by the core Tensor library
 
 * Core features
-  * n-dimensional arrays (tensors) in host memory or on CUDA GPUs 
+  * n-dimensional arrays (tensors) in host memory or on CUDA GPUs
   * element-wise operations (addition, multiplication, absolute value, etc.)
   * basic linear algebra operations (dot product, SVD decomposition, matrix inverse, etc.)
   * reduction operations (sum, product, average, maximum, arg max, etc.)
-  * logic operations (comparision, and, or, etc.)
-  * views, slicing, reshaping, broadcasting (similar to NumPy) 
+  * logic operations (comparison, and, or, etc.)
+  * views, slicing, reshaping, broadcasting (similar to NumPy)
   * scatter and gather by indices
   * standard functional operations (map, fold, etc.)
 * Data exchange
@@ -37,7 +37,7 @@ This open source library is written in [F#](http://fsharp.org/) and targets the
 * Matrix algebra (integer, rational)
   * Row echelon form
   * Smith normal form
-  * Kernel, cokernel and (pseudo-)inverse
+  * Kernel, co-kernel and (pseudo-)inverse
 * Matrix decomposition (floating point)
   * Principal component analysis (PCA)
   * ZCA whitening
@@ -59,4 +59,3 @@ The following NuGet packages are available for download.
 ## Documentation
 
 Documentation is provided at <http://www.deepml.net/Tensor>.
-

Tensor/Tensor.Docs/articles/ReleaseNotes.md

@@ -1,8 +1,11 @@
 # Release notes
 
+* 0.4.11
+  * First release of 0.4 branch on public NuGet.
+
 * 0.4.10
   * Update to CUDA 9.1
-  * CUDA SDK no longer required. 
+  * CUDA SDK no longer required.
 
 * 0.4.9
   * Remove type constraints on Tensor.arange and Tensor.linspace.

Tensor/Tensor.Docs/index.md

@@ -6,7 +6,7 @@ A *tensor* is an n-dimensional array of an arbitrary data type (for example `sin
 Tensors of data type `'T` are implemented by the [Tensor<'T>](xref:Tensor.Tensor`1) type.
 
 A tensor can be either stored in host memory or in the memory of a GPU computing device.
-Currenty only nVidia cards implementing the [CUDA API](https://developer.nvidia.com/cuda-zone) are supported.
+Currently only nVidia cards implementing the [CUDA API](https://developer.nvidia.com/cuda-zone) are supported.
 The API for host and GPU stored tensors is mostly equal, thus a program can make use of GPU accelerated operations without porting effort.
 
 The tensor library provides functionality similar to [Numpy's Ndarray](http://docs.scipy.org/doc/numpy-1.10.0/reference/arrays.html) and [MATLAB arrays](http://www.mathworks.com/help/matlab/matrices-and-arrays.html), including vector-wise operations, reshaping, slicing, broadcasting, masked assignment, reduction operations and BLAS operations.
@@ -36,7 +36,7 @@ This open source library is written in [F#](http://fsharp.org/) and targets the
 * Matrix algebra (integer, rational)
   * Row echelon form
   * Smith normal form
-  * Kernel, cokernel and (pseudo-)inverse
+  * Kernel, co-kernel and (pseudo-)inverse
 * Matrix decomposition (floating point)
   * Principal component analysis (PCA)
   * ZCA whitening

Tensor/Tensor.Sample/README.md

@@ -3,13 +3,13 @@
 [![Build Status](https://travis-ci.org/DeepMLNet/Tensor.Sample.svg?branch=master)](https://travis-ci.org/DeepMLNet/Tensor.Sample)
 [![Build status](https://ci.appveyor.com/api/projects/status/wrun3ku66kl09ki6?svg=true)](https://ci.appveyor.com/project/surban/tensor-sample)
 
-This is an example project that demonstrates the capabilites of the [F# tensor library](http://www.deepml.net/Tensor).
+This is an example project that demonstrates the capabilities of the [F# tensor library](http://www.deepml.net/Tensor).
 
 ## System requirements
 
-* Linux or Microsoft Windows (64-bit)
+* Linux, MacOS or Microsoft Windows (64-bit)
 * [Microsoft .NET Core 2.0 or higher](https://www.microsoft.com/net/learn/get-started/)
-* For GPU acceleration (optional): [nVidia CUDA SDK 8.0](https://developer.nvidia.com/cuda-80-ga2-download-archive)
+* For GPU acceleration (optional): [nVidia CUDA GPU with recent driver](http://www.nvidia.com/Download/index.aspx)
 
 ## Running