Update README.md

README.md

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-# KerasHub: Multi-framework Models
+# KerasHub: Multi-framework Pretrained Models
 [![](https://github.com/keras-team/keras-hub/workflows/Tests/badge.svg?branch=master)](https://github.com/keras-team/keras-hub/actions?query=workflow%3ATests+branch%3Amaster)
 ![Python](https://img.shields.io/badge/python-v3.9.0+-success.svg)
 [![contributions welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat)](https://github.com/keras-team/keras-hub/issues)
@@ -7,98 +7,115 @@
 > 📢 KerasNLP is now KerasHub! 📢 Read
 > [the announcement](https://github.com/keras-team/keras-hub/issues/1831).
 
-KerasHub is a library that supports natural language processing, computer
-vision, audio, and multimodal backbones and task models, working natively with
-TensorFlow, JAX, or PyTorch. KerasHub provides a repository of pre-trained
-models and a collection of lower-level building blocks for these tasks. Built
-on Keras 3, models can be trained and serialized in any framework and re-used
-in another without costly migrations.
+**KerasHub** is a pretrained modeling library that aims to be simple, flexible,
+and fast. The library provides [Keras 3](https://keras.io/keras_3/)
+implementations of popular model architectures, paired with a collection of
+pretrained checkpoints available on [Kaggle Models](https://kaggle.com/models/).
+Models can be used with text, image, and audio data for generation, classification,
+and many other built in tasks.
 
-This library is an extension of the core Keras API; all high-level modules are
-Layers and Models that receive that same level of polish as core Keras.
-If you are familiar with Keras, congratulations! You already understand most of
-KerasHub.
+KerasHub is an extension of the core Keras API; KerasHub components are provided
+as `Layer` and `Model` implementations. If you are  familiar with Keras,
+congratulations! You already understand most of KerasHub.
 
 All models support JAX, TensorFlow, and PyTorch from a single model
 definition and can be fine-tuned on GPUs and TPUs out of the box. Models can
 be trained on individual accelerators with built-in PEFT techniques, or
 fine-tuned at scale with model and data parallel training. See our
 [Getting Started guide](https://keras.io/guides/keras_hub/getting_started)
-to start learning our API. Browse our models on
-[Kaggle](https://www.kaggle.com/organizations/keras/models).
-We welcome contributions.
+to start learning our API.
 
 ## Quick Links
 
 ### For everyone
 
-- [Home Page](https://keras.io/keras_hub)
-- [Developer Guides](https://keras.io/guides/keras_hub)
-- [API Reference](https://keras.io/api/keras_hub)
-- [Pre-trained Models](https://www.kaggle.com/organizations/keras/models)
+- [Home page](https://keras.io/keras_hub)
+- [Getting started](https://keras.io/keras_hub/getting_started)
+- [Guides](https://keras.io/keras_hub/guides)
+- [API documentation](https://keras.io/keras_hub/api)
+- [Pre-trained models](https://keras.io/keras_hub/presets/)
 
 ### For contributors
 
+- [Call for Contributions](https://github.com/keras-team/keras-hub/issues/1835)
+- [Roadmap](https://github.com/keras-team/keras-hub/issues/1836)
 - [Contributing Guide](CONTRIBUTING.md)
-- [Roadmap](ROADMAP.md)
 - [Style Guide](STYLE_GUIDE.md)
 - [API Design Guide](API_DESIGN_GUIDE.md)
-- [Call for Contributions](https://github.com/keras-team/keras-hub/issues?q=is%3Aissue+is%3Aopen+label%3A%22contributions+welcome%22)
 
 ## Quickstart
 
-Fine-tune a BERT classifier on IMDb movie reviews:
+Choose a backend:
 
 ```python
 import os
 os.environ["KERAS_BACKEND"] = "jax"  # Or "tensorflow" or "torch"!
+```
+
+Import KerasHub and other libraries:
 
+```python
+import keras
 import keras_hub
+import numpy as np
 import tensorflow_datasets as tfds
+```
 
+Load a resnet model and use it to predict a label for an image:
+
+```python
+classifier = keras_hub.models.ImageClassifier.from_preset(
+    "resnet_50_imagenet",
+    activation="softmax",
+)
+url = "https://upload.wikimedia.org/wikipedia/commons/a/aa/California_quail.jpg"
+path = keras.utils.get_file(origin=url)
+image = keras.utils.load_img(path)
+preds = classifier.predict(np.array([image]))
+print(keras_hub.utils.decode_imagenet_predictions(preds))
+```
+
+Load a Bert model and fine-tune it on IMDb movie reviews:
+
+```python
+classifier = keras_hub.models.BertClassifier.from_preset(
+    "bert_base_en_uncased",
+    activation="softmax",
+    num_classes=2,
+)
 imdb_train, imdb_test = tfds.load(
     "imdb_reviews",
     split=["train", "test"],
     as_supervised=True,
     batch_size=16,
 )
-
-# Load a BERT model.
-classifier = keras_hub.models.Classifier.from_preset(
-    "bert_base_en",
-    num_classes=2,
-    activation="softmax",
-)
-
-# Fine-tune on IMDb movie reviews.
 classifier.fit(imdb_train, validation_data=imdb_test)
-# Predict two new examples.
-classifier.predict(["What an amazing movie!", "A total waste of my time."])
+preds = classifier.predict(["What an amazing movie!", "A total waste of time."])
+print(preds)
 ```
 
-Try it out [in a colab](https://colab.research.google.com/drive/1gSWkh3yOLwmKAaNh2dQQ6kQIlnGte7P2?usp=sharing).
-For more in depth guides and examples, visit
-[keras.io/keras_hub](https://keras.io/keras_hub/).
-
 ## Installation
 
-To try out the latest version of KerasHub, you can use
-our nightly package:
+To install the latest KerasHub release with Keras 3, simply run:
 
-```bash
-pip install keras-hub
 ```
+pip install --upgrade keras-hub
+```
+
+To install the latest nightly changes for both KerasHub and Keras, you can use
+our nightly package.
 
-KerasHub currently requires TensorFlow to be installed for use of the
-`tf.data` API for preprocessing. Even when pre-processing with `tf.data`,
-training can still happen on any backend.
+```
+pip install --upgrade keras-hub-nightly
+```
 
-Read [Getting started with Keras](https://keras.io/getting_started/) for more
-information on installing Keras 3 and compatibility with different frameworks.
+Currently, installing KerasHub will always pull in TensorFlow for use of the
+`tf.data` API for preprocessing. When pre-processing with `tf.data`, training
+can still happen on any backend.
 
-> [!IMPORTANT]
-> We recommend using KerasHub with TensorFlow 2.16 or later, as TF 2.16 packages
-> Keras 3 by default.
+Visit the [core Keras getting started page](https://keras.io/getting_started/)
+for more information on installing Keras 3, accelerator support, and
+compatibility with different frameworks.
 
 ## Configuring your backend