Update docs

Author: ekrich

LICENSE.md

@@ -14,52 +14,18 @@ limitations under the License.
 
 ### Origin of this work
 
-This project brings BLAS (Basic Linear Algebra Subprograms) to Scala Native.
-The code is written in Scala to interface with the C library interface to BLAS
-known as CBLAS. As such this is a derivative work so the applicable licenses
+This project brings the TensorFlow to Scala Native.
+The code is written in Scala to interface with the C library interface 
+to TensorFlow. As such this is a derivative work so the applicable licenses
 follow to attribute to the original work as best as can be determined.
 
-### License notice for BLAS
+### License notice for TensorFlow
 
-The reference BLAS is a freely-available software package. It is available from
-netlib via anonymous ftp and the World Wide Web. Thus, it can be included in
-commercial software packages (and has been). We only ask that proper credit
-be given to the authors.
-
-Like all software, it is copyrighted. It is not trademarked, but we do
-ask the following:
-
-- If you modify the source for these routines we ask that you change the
-name of the routine and comment the changes made to the original.
-
-- We will gladly answer any questions regarding the software. If a modification
-is done, however, it is the responsibility of the person who modified the
-routine to provide support.
-
-Please reference the following for information and history about the
-project: http://www.netlib.org/blas/
+TensorFlow is developed by Google and licensed via the Apache License,
+Version 2.0.
 
 
 ### License notice for Documentation
 
-The documentation comes from multiple sources as listed below. There are many
-operating systems and vendors that supply BLAS/CBLAS so the list includes ones
-that have been referenced for understanding, documentation, and creating tests.
-
-Wikipedia Website:
-- https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms
-
-BLAS Website:
-- http://www.netlib.org/blas/#_documentation
-- http://www.netlib.org/blas/#_blas_routines
-
-Apple Website:
-- https://developer.apple.com/documentation/accelerate/blas?language=objc
-
-Intel Website:
-- https://software.intel.com/en-us/mkl-developer-reference-c-blas-routines
-- https://software.intel.com/en-us/mkl-developer-reference-c-naming-conventions-for-blas-routines
-
-IBM Website:
-- https://www.ibm.com/support/knowledgecenter/en/SSFHY8_6.1/reference/am5gr_apa.html
-
+The documentation comes from source files and the TensorFlow
+website and should be attributed to the original sources.

README.md

@@ -1,12 +1,14 @@
 # stensorflow - Scala Native TensorFlow
 [![Build Status](https://travis-ci.org/ekrich/sblas.svg?branch=master)](https://travis-ci.org/ekrich/stensorflow)
 
-This library implements TensorFlow 1.13.1 (Basic Linear Algebra Subprograms) in the form
-of CBLAS for the Scala Native platform. Scala Native is a unique platform that
+This library implements TensorFlow version `1.13.1` in the form of C TensorFlow
+API adapted for the Scala Native platform. 
+
+Scala Native is a unique platform that
 marries the high level language of Scala but compiles to native code with a
 lightweight managed runtime which includes a state of the art garbage collector.
 The combination allows for great programming and the ability to use high
-performance C language libraries like CBLAS.
+performance C language libraries like TensorFlow.
 
 Scala Native uses the Scala compiler to produce
 [NIR](https://scala-native.readthedocs.io/en/latest/contrib/nir.html)
@@ -15,12 +17,12 @@ converted to [LLVM IR](http://llvm.org/). Finally LLVM code is optimized
 and compiled by [Clang](http://clang.llvm.org/) to produce a native executable.
 
 ## Getting started
-[![Maven Central](https://img.shields.io/maven-central/v/org.ekrich/sblas_native0.3_2.11.svg)](https://maven-badges.herokuapp.com/maven-central/org.ekrich/sblas_native0.3_2.11)
+[![Maven Central](https://img.shields.io/maven-central/v/org.ekrich/stensorflow_native0.3_2.11.svg)](https://maven-badges.herokuapp.com/maven-central/org.ekrich/stensorflow_native0.3_2.11)
 
 If you are already familiar with Scala Native you can jump right in by adding the following dependency in your `sbt` build file.
 
 ```scala
-libraryDependencies += "org.ekrich" %%% "sblas" % "x.y.z"
+libraryDependencies += "org.ekrich" %%% "stensorflow" % "x.y.z"
 ```
 
 To use in `sbt`, replace `x.y.z` with the version from Maven Central badge above.
@@ -31,13 +33,12 @@ instructions for Scala Native if you are not already setup.
 
 Additional libraries that need to be installed on you system are as follows:
 
-* Linux/Ubuntu can TensorFlow as follows:
+* Linux/Ubuntu can TensorFlow following the following directions:
 
-```
-$ sudo apt-get install libatlas-base-dev
-```
+https://www.tensorflow.org/install/lang_c
 
-* macOS can install TensorFlow using [Homebrew](https://formulae.brew.sh/formula/libtensorflow) which will install into the `/usr/local/Cellar/libtensorflow/<version>` directory.
+* macOS can install TensorFlow using [Homebrew](https://formulae.brew.sh/formula/libtensorflow) 
+which will install into the `/usr/local/Cellar/libtensorflow/<version>` directory.
 
 ```
 $ brew install libtensorflow
@@ -46,43 +47,25 @@ $ brew install libtensorflow
 * Other OSes need to have `libtensorflow` available on the system.
 
 ## Usage and Help
-[![scaladoc](https://www.javadoc.io/badge/org.ekrich/sblas_native0.3_2.11.svg?label=scaladoc)](https://www.javadoc.io/doc/org.ekrich/sblas_native0.3_2.11)
-[![Join chat https://gitter.im/ekrich/sblas](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/ekrich/sblas?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
+[![scaladoc](https://www.javadoc.io/badge/org.ekrich/stensorflow_native0.3_2.11.svg?label=scaladoc)](https://www.javadoc.io/doc/org.ekrich/stensorflow_native0.3_2.11)
+[![Join chat https://gitter.im/ekrich/stensorflow](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/ekrich/stensorflow?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
 
 Reference the link above for Scaladoc. The documentation is a little sparse but hopefully will improve with time.
 
-After `sbt` is installed and any other Scala Native prerequisites are met you can use the following Gitter G8 template instructions to get a fully functional Scala Native application with a couple of BLAS examples in the body of the main program.
+After `sbt` is installed and any other Scala Native prerequisites are met you can use the following Gitter G8 template instructions to get a fully functional Scala Native application with an example in the body of the main program.
 
 ```
-$ sbt new ekrich/sblas.g8
+$ sbt new ekrich/stensorflow.g8
 $ cd <directory entered after the prompt>
 $ sbt run
 ```
 
-In addition, look at the `v0.1.1` [sblas unit tests](https://github.com/ekrich/sblas/blob/v0.1.1/sblas/src/test/scala/org/ekrich/blas/snic/BlasSuite.scala) for other examples of usage.
-
-## BLAS References and External Documentation
-
-Some useful links are as follows which are also in the LICENSE.md file as some of the Scaladoc was sourced from these references:
-
-Wikipedia Website:
-- https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms
-
-Netlib Website:
-- http://www.netlib.org/blas/#_documentation
-- http://www.netlib.org/blas/#_blas_routines
-- http://www.netlib.org/lapack/lapack-3.1.1/html/
-
-Apple Website:
-- https://developer.apple.com/documentation/accelerate/blas?language=objc
+In addition, look at the `v0.1.0` [stensorflow unit tests](https://github.com/ekrich/stensorflow/blob/v0.1.0/stensorflow/src/test/scala/org/ekrich/stensorflow/snic/TensorflowSuite.scala) for other examples of usage.
 
-Intel Website:
-- https://software.intel.com/en-us/mkl-developer-reference-c-blas-routines
-- https://software.intel.com/en-us/mkl-developer-reference-c-naming-conventions-for-blas-routines
+## TensorFlow References and External Documentation
 
-IBM Website:
-- https://www.ibm.com/support/knowledgecenter/en/SSFHY8_6.1/reference/am5gr_apa.html
+[TensorFlow Website](https://www.tensorflow.org/)
 
 ## Versions
 
-Release [0.1.1](https://github.com/ekrich/sblas/releases/tag/v0.1.1) - (2019-05-01)<br/>
+Release [0.1.0](https://github.com/ekrich/tensorflow/releases/tag/v0.1.0) - (2019-XX-XX)<br/>