Editorial pass of C# tutorial

docs/tutorials/csharp/basic_csharp.md

@@ -10,73 +10,67 @@ nav_order: 1
 
 # C# Tutorial: Basic
 
-Here is a simple tutorial for getting started with running inference on an existing ONNX model for a given input data.
-The model is typically trained using any of the well-known training frameworks and then exported into the ONNX format. 
+Learn how to get started with inference with the C# API. Run an ONNX model for a given input data. The model is typically trained using any of the well-known training frameworks and then exported into the ONNX format.
 
-Note, that the following classes `NamedOnnxValue`, `DisposableNamedOnnxValue`, `FixedBufferOnnxValue` are going
-to be deprecated in the future. They are not recommended for new code.
+## OrtValue API
 
-The new `OrtValue` based API is the recommended approach. The `OrtValue` API generates less garbage and is more performant.
-Some scenarios indicated 4x performance improvement over the previous API and significantly less garbage.
-It provides uniform access to data via `ReadOnlySpan<T>` and `Span<T>` structures, regardless of its location, managed or unmanaged.
+The new `OrtValue` based API is the recommended approach. The `OrtValue` API generates less garbage and is more performant. Some scenarios show 4x performance improvement over the previous API and significantly less garbage.
 
-`DenseTensor` class can still be used for multi-dimensional access to the data since the new `Span` based API feature
-only a 1-D index. However, some reported a slow performance when using `DenseTensor` class multi-dimensional access.
-One can then create an OrtValue on top of the tensors data. 
+OrtValue is a universal container that can hold different ONNX types, such as tensors, maps, and sequences. It always existed in the onnxruntime library, but was not exposed in the C# API.
+
+The `OrtValue` based API provides uniform access to data via `ReadOnlySpan<T>` and `Span<T>` structures, regardless of its location, managed or unmanaged.
+
+Note, that the following classes `NamedOnnxValue`, `DisposableNamedOnnxValue`, `FixedBufferOnnxValue` will be deprecated in the future. They are not recommended for new code.
+
+## Data shape
+
+`DenseTensor` class can be used for multi-dimensional access to the data since the new `Span` based API features only a 1-D index. However, some reported a slow performance when using `DenseTensor` class multi-dimensional access. One can then create an OrtValue on top of the tensors data. 
 
 `ShapeUtils` class provides some help to deal with multi-dimensional indices for OrtValues.
 
-`OrtValue` based API provides direct native memory access in a type safe manner using `ReadOnlySpan<T>` and `Span<T>` stack bases structures.
-OrtValue is a universal container that can hold different ONNX types, such as tensors, maps, and sequences.
-It always existed in the onnxruntime library, but was not exposed in the C# API.
+If output shapes are known, one can pre-allocate `OrtValue` on top of the managed or unmanaged allocations and supply those OrtValues to be used as outputs. Due to this fact, the need for `IOBinding` is greatly diminished.
+
+
+## Data types
+
+`OrtValues` can be created directly on top of the managed `unmanaged` [struct based blittable types](https://learn.microsoft.com/en-us/dotnet/framework/interop/blittable-and-non-blittable-types) arrays. The onnxruntime C# API allows use of managed buffers for input or output.
 
-As before, `OrtValues` can be created directly on top of the managed `unmanaged` (struct based blittable types)  arrays.
-Read MS documentation on `blittable` data types. onnxruntime C# API allows use of managed buffers for input or output.
+String data is represented as UTF-16 string objects in C#. It will still need to be copied and converted to UTF-8 to the native memory. However, that conversion is now more optimized and is done in a single pass without intermediate byte arrays.
 
-If output shapes are known, one can pre-allocate `OrtValue` on top of the managed or unmanaged allocations and supply
-those OrtValues to be used as outputs. Due to this fact, the need for `IOBinding` is greatly diminished.
+The same applies to string `OrtValue` tensors returned as outputs. Character based API now operates on `Span<char>`,`ReadOnlySpan<char>`, and `ReadOnlyMemory<char>` objects. This adds flexibility to the API and allows to avoid unnecessary copies.
 
-String data is represented as UTF-16 string objects in C#. It will still need to be copied and converted to UTF-8 to the native
-memory. However, that conversion is now more optimized and is done in a single pass without intermediate byte arrays.
-The same applies to string `OrtValue` tensors returned as outputs. Character based API now operates on `Span<char>`,
-`ReadOnlySpan<char>`, and `ReadOnlyMemory<char>` objects. This adds flexibility to the API and allows to avoid unnecessary copies.
+## Data life-cycle
 
-Except some of the above deprecated API classes, nearly all of C# API classes are `IDisposable`.
-Meaning they need to be disposed after use, otherwise you will get memory leaks.
-Because OrtValues are used to hold tensor data, the sizes of the leaks can be huge. They are likely
-to accumulate with each `Run` call, as each inference call requires input OrtValues and returns output OrtValues.
-Do not hold your breath for finalizers which are not guaranteed to ever run, and if they do, they do it
-when it is too late.
+Except for some of the above deprecated API classes, nearly all of C# API classes are `IDisposable`.
+Meaning they need to be disposed after use, otherwise you will get memory leaks. Because OrtValues are used to hold tensor data, the sizes of the leaks can be huge. They are likely to accumulate with each `Run` call, as each inference call requires input OrtValues and returns output OrtValues.
+Do not hold your breath for finalizers which are not guaranteed to ever run, and if they do, they do it when it is too late.
 
-This includes `SessionOptions`, `RunOptions`, `InferenceSession`, `OrtValue`. Run() calls return `IDisposableCollection`
-that allows to dispose all of the containing objects in one statement or `using`. This is because these objects
-own some native resource, often a native object.
+This includes `SessionOptions`, `RunOptions`, `InferenceSession`, `OrtValue`. Run() calls return `IDisposableCollection` that allows to dispose all of the containing objects in one statement or `using`. This is because these objects own native resources, often a native object.
 
 Not disposing `OrtValue` that was created on top of the managed buffer would result in
 that buffer pinned in memory indefinitely. Such a buffer can not be garbage collected or moved in memory.
 
-`OrtValue`s that were created on top of the native onnxruntime memory should also be disposed of promptly.
-Otherwise, the native memory will not be deallocated. OrtValues returned by `Run()` usually hold native memory.
+`OrtValue`s that were created on top of the native onnxruntime memory should also be disposed of promptly. Otherwise, the native memory will not be deallocated. OrtValues returned by `Run()` usually hold native memory.
 
 GC can not operate on native memory or any other native resources.
 
 The `using` statement or a block is a convenient way to ensure that the objects are disposed.
-`InferenceSession` can be a long lived object and a member of another class. It eventually must also need to be disposed.
-This means, the containing class also would have to be made disposable to achieve this.
+`InferenceSession` can be a long lived object and a member of another class. It eventually must also need to be disposed. This means, the containing class also would have to be made disposable to achieve this.
 
 OrtValue API also provides visitor like API to walk ONNX maps and sequences.
 This is a more efficient way to access Onnxruntime data.
 
-To start scoring using the model, open a session using the `InferenceSession` class, passing in the file path to the model as a parameter.
+## Code example to run a model
+
+To start scoring using the model, create a session using the `InferenceSession` class, passing in the file path to the model as a parameter.
 
 ```cs
 using var session = new InferenceSession("model.onnx");
 ```
 
-Once a session is created, you can execute queries using the `Run` method of the  `InferenceSession` object.
+Once a session is created, you can run inference using the `Run` method of the  `InferenceSession` object.
 
 ```cs
-
 float[] sourceData;  // assume your data is loaded into a flat float array
 long[] dimensions;    // and the dimensions of the input is stored here
 
@@ -107,11 +101,11 @@ var outputData = output_0.GetTensorDataAsSpan<float>();
 var tensorTypeAndShape = output_0.GetTensorTypeAndShape();
 
 ```
+
 You can still use `Tensor` class for data manipulation if you have existing code that does it.
 Then create `OrtValue` on top of Tensor buffer.
 
 ```cs
-
 // Create and manipulate the data using tensor interface
 DenseTensor<float> t1 = new DenseTensor<float>(sourceData, dimensions);
 
@@ -127,13 +121,10 @@ using var inputOrtValue = OrtValue.CreateTensorValueFromMemory(OrtMemoryInfo.Def
 
 ```
 
-Here is a way to populate a string tensor. Strings can not be mapped, and must be copy/converted to native memory.
-To that end we pre-allocate a native tensor of empty strings with specified dimensions, and then
-set individual strings by index.
+Here is a way to populate a string tensor. Strings can not be mapped, and must be copy/converted to native memory. To that end we pre-allocate a native tensor of empty strings with specified dimensions, and then set individual strings by index.
 
 
 ```cs
-
 string[] strs = { "Hello", "Ort", "World" };
 long[] shape = { 1, 1, 3 };
 var elementsNum = ShapeUtils.GetSizeForShape(shape);
@@ -144,7 +135,14 @@ for (long i = 0; i < elementsNum; ++i)
 {
     strTensor.StringTensorSetElementAt(strs[i].AsSpan(), i);
 }
-
 ```
 
+## More examples
+
+* [Stable Diffusion](stable-diffusion-csharp.md)
+* [BERT NLP example](bert-nlp-csharp-console-app.md)
+* [Run on GPU](csharp-gpu.md)
+* [Yolov3](yolov3_object_detection_csharp.md)
+* [Faster CNN](fasterrcnn_csharp.md)
+* [Resnet 50](resnet50_csharp.md)