improve README writing, fix links, remove dead weight

README.md

@@ -5,16 +5,16 @@
 [![License](https://img.shields.io/pypi/l/neural-pipeline-search?color=informational)](LICENSE)
 [![Tests](https://github.com/automl/neps/actions/workflows/tests.yaml/badge.svg)](https://github.com/automl/neps/actions)
 
-Welcome to NePS, a powerful and flexible Python library for hyperparameter optimization (HPO) and neural architecture search (NAS) with its primary goal: enable HPO adoption in practice for deep learners!
+Welcome to NePS, a powerful and flexible Python library for hyperparameter optimization (HPO) and neural architecture search (NAS) with its primary goal: enable HPO and NAS for deep learners!
 
-NePS houses recently published and some more well-established algorithms that are all capable of being run massively parallel on any distributed setup, with tools to analyze runs, restart runs, etc.
+NePS houses recently published and well-established algorithms that can all be run massively parallel on distributed setups, with tools to analyze runs, restart runs, etc., all tailored to the needs of deep learning experts.
 
-Take a look at our [documentation](https://automl.github.io/neps/latest/) and continue following through current README for instructions on how to use NePS!
+Take a look at our [documentation](https://automl.github.io/neps/latest/) for all the details on how to use NePS!
 
 
 ## Key Features
 
-In addition to the common features offered by traditional HPO and NAS libraries, NePS stands out with the following key features:
+In addition to the common features offered by traditional HPO and NAS libraries, NePS stands out with:
 
 1. [**Hyperparameter Optimization (HPO) With Prior Knowledge:**](neps_examples/template/priorband_template.py)
     - NePS excels in efficiently tuning hyperparameters using algorithms that enable users to make use of their prior knowledge within the search space. This is leveraged by the insights presented in:
@@ -89,38 +89,29 @@ def run_pipeline(
     }
 
 
-# 2. Define a search space of parameters; use the same names for the parameters as in run_pipeline
+# 2. Define a search space of parameters; use the same parameter names as in run_pipeline
 pipeline_space = dict(
-    hyperparameter_b=neps.IntegerParameter(
-        lower=1, upper=42, is_fidelity=True
-    ),  # Mark 'is_fidelity' as true for a multi-fidelity approach.
     hyperparameter_a=neps.FloatParameter(
-        lower=0.001, upper=0.1, log=True
-    ),  # If True, the search space is sampled in log space.
-    architecture_parameter=neps.CategoricalParameter(
-        ["option_a", "option_b", "option_c"]
+        lower=0.001, upper=0.1, log=True  # The search space is sampled in log space
     ),
+    hyperparameter_b=neps.IntegerParameter(lower=1, upper=42),
+    architecture_parameter=neps.CategoricalParameter(["option_a", "option_b"]),
 )
 
-if __name__ == "__main__":
-    # 3. Run the NePS optimization
-    logging.basicConfig(level=logging.INFO)
-    neps.run(
-        run_pipeline=run_pipeline,
-        pipeline_space=pipeline_space,
-        root_directory="path/to/save/results",  # Replace with the actual path.
-        max_evaluations_total=100,
-        searcher="hyperband"  # Optional specifies the search strategy,
-        # otherwise NePs decides based on your data.
-    )
+
+# 3. Run the NePS optimization
+logging.basicConfig(level=logging.INFO)
+neps.run(
+    run_pipeline=run_pipeline,
+    pipeline_space=pipeline_space,
+    root_directory="path/to/save/results",  # Replace with the actual path.
+    max_evaluations_total=100,
+)
 ```
 
 ## Examples
 
 Discover how NePS works through these practical examples:
-* **[Pipeline Space via YAML](neps_examples/basic_usage/hpo_usage_example.py)**: Explore how to define the `pipeline_space` using a
-  YAML file instead of a dictionary.
-
 * **[Hyperparameter Optimization (HPO)](neps_examples/basic_usage/hyperparameters.py)**: Learn the essentials of hyperparameter optimization with NePS.
 
 * **[Architecture Search with Primitives](neps_examples/basic_usage/architecture.py)**: Dive into architecture search using primitives in NePS.
@@ -131,24 +122,10 @@ Discover how NePS works through these practical examples:
 
 * **[Additional NePS Examples](neps_examples/)**: Explore more examples, including various use cases and advanced configurations in NePS.
 
-## Documentation
-
-For more details and features please have a look at our [documentation](https://automl.github.io/neps/latest/)
-
-## Analysing runs
-
-See our [documentation on analysing runs](https://automl.github.io/neps/latest/analyse).
-
 ## Contributing
 
-Please see the [documentation for contributors](https://automl.github.io/neps/latest/contributing/).
+Please see the [documentation for contributors](https://automl.github.io/neps/latest/dev_docs/contributing/).
 
 ## Citations
 
-Please consider citing us if you use our tool!
-
-Refer to our [documentation on citations](https://automl.github.io/neps/latest/citations/).
-
-## Alternatives
-
-NePS does not cover your use-case? Have a look at [some alternatives](https://automl.github.io/neps/latest/alternatives).
+For pointers on citing the NePS package and papers refer to our [documentation on citations](https://automl.github.io/neps/latest/citations/).