Updated README.md to describe our API layers. (tensorflow#108)

deansher · web-flow · commit 740fa0b204ec · 2020-09-09T09:15:07.000-04:00
* Updated README.md to describe our API layers.

* Updated README.md to state that `tensorflow-keras` is early WIP; only defined in `dev` profile.
diff --git a/README.md b/README.md
@@ -25,16 +25,29 @@ migrated from Bazel to Maven, which is more familiar for most Java developers.
 The following describes the layout of the repository and its different artifacts:
 
 * `tensorflow-core`
-  * All artifacts that build up the core language bindings of TensorFlow for Java. 
-  * Those artifacts provide the minimal support required to use the TensorFlow runtime on a JVM.
+  * All artifacts that build up the core language bindings of TensorFlow for Java
+  * Intended audience: projects that provide their own APIs or frameworks on top of 
+    TensorFlow and just want a thin layer to access the TensorFlow runtime from the JVM 
     
 * `tensorflow-framework`
-  * High-level APIs built on top of the core libraries to simplify neural network training and inference 
-    using TensorFlow.
+  * Complete but fairly primitive API for building and training neural networks with TensorFlow
+  * Intended audience: expert neural network developers who prefer to make explicit, detailed decisions 
+    about their models and training algorithms
+    
+* `tensorflow-keras` (early WIP; only defined in `dev` profile)
+  * Partially covers the framework API to allow simpler definition of models and training algorithms
+  * Intended to be familiar if you know the Python Keras API, but prioritizes clean, idiomatic Java 
+    over fidelity to Python
+  * Provides defaults based on common best practices
+  * Allows developers to selectively be more explicit by overriding defaults or dipping into the framework API
+  * Intended audience: neural network developers across the spectrum from beginner to expert who prefer to
+    rely mostly on best-practice defaults and then selectively fine-tune
   
 * `ndarray`
-  * Generic utility library for n-dimensional data I/O operations. It is used by TensorFlow without depending
-    on it, making its usage eligible to any type of projects, using TensorFlow or not.
+  * Generic utility library for n-dimensional data I/O operations
+  * Used by TensorFlow but does not depend on TensorFlow
+  * Intended audience: any developer who needs a Java n-dimensional array implementation, whether or not they
+    use it with TensorFlow
   
 ## Building Sources
 
