Edits

_posts/2024-05-21-keras3/introducing-keras3.Rmd

@@ -64,19 +64,21 @@ up-to-date:
     With each release, all R documentation is rebased on upstream
     updates. This workflow ensures that all R documentation (guides,
     examples, vignettes, and reference pages) and R function signatures
-    stay up-to-date with upstream. This snapshot+rebase functionality is
-    implemented in a standalone R package,
-    [{doctether}](https://github.com/t-kalinowski/doctether)
+    stay up-to-date with upstream. This snapshot-and-rebase
+    functionality is implemented in a new standalone R package,
+    [{doctether}](https://github.com/t-kalinowski/doctether), which may be
+    useful for R package maintainers needing to keep documentation in
+    parity with dependencies.
 
 -   All examples and vignettes can now be evaluated and rendered during
     a package build. This ensures that no stale or broken example code
-    makes it into a release. The also means that all user facing example
-    code now additionally serves as an extended suite of snapshot unit
-    and integration tests.
+    makes it into a release. It also means all user facing example code
+    now additionally serves as an extended suite of snapshot unit and
+    integration tests.
 
-    Evaluating code in vignettes and examples continues to be forbidden
-    by to CRAN restrictions. We work around the CRAN restriction by
-    adding additional package build steps that pre-render
+    Evaluating code in vignettes and examples is still not permitted
+    according to CRAN restrictions. We work around the CRAN restriction
+    by adding additional package build steps that pre-render
     [examples](https://github.com/rstudio/keras/blob/main/man/roxygen/meta.R)
     and
     [vignettes](https://github.com/rstudio/keras/blob/main/tools/knit.R).
@@ -87,13 +89,12 @@ Python API to Keras.
 
 ### Multi-backend support
 
-Keras v1 started life as a library that worked with multiple backends.
-Back then, TensorFlow, Theano, and Caffe were the most popular
-frameworks for deep learning, and the frameworks that Keras supported.
-Over time, the landscape shifted; Theano and Caffe were retired, and
-TensorFlow surged in popularity. With Keras v2, TensorFlow became the
-premier and only supported backend. Now, the landscape has shifted
-again.
+Soon after its launch in 2015, Keras featured support for most popular
+deep learning frameworks: TensorFlow, Theano, MXNet, and CNTK. Over
+time, the landscape shifted; Theano, MXNet, and CNTK were retired, and
+TensorFlow surged in popularity. In 2021, three years ago, TensorFlow
+became the premier and only supported Keras backend. Now, the landscape
+has shifted again.
 
 Keras 3 brings the return of multi-backend support. Choose a backend by
 calling:
@@ -286,11 +287,11 @@ as **`layer_dense()`**, **`fit()`**, and **`keras_model()`**, minimal to
 no changes are required. However there is a long tail of small changes
 that you might need to make when updating code that made use of the
 lower-level Keras API. Some of those are documented here:
-https://keras.io/guides/migrating_to_keras_3/.
+<https://keras.io/guides/migrating_to_keras_3/>.
 
 If you're running into issues or have questions about updating, don't
-hesitate to ask on https://github.com/rstudio/keras/issues or
-https://github.com/rstudio/keras/discussions.
+hesitate to ask on <https://github.com/rstudio/keras/issues> or
+<https://github.com/rstudio/keras/discussions>.
 
 The `{keras}` and `{keras3}` packages will coexist while the community
 transitions. During the transition, `{keras}` will continue to receive