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[DOCS] Fix links to components (openvinotoolkit#24339)
### Details: - *item1* - *...* ### Tickets: - *ticket-id*
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README.md

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@@ -100,9 +100,9 @@ Check out the [Awesome OpenVINO](https://github.com/openvinotoolkit/awesome-open
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## Documentation
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[**User documentation**](https://docs.openvino.ai/) contains detailed information about OpenVINO and guides you from installation through optimizing and deploying models for your AI applications.
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[User documentation](https://docs.openvino.ai/) contains detailed information about OpenVINO and guides you from installation through optimizing and deploying models for your AI applications.
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[**Developer documentation**](./docs/dev/index.md) focuses on how OpenVINO [components](./docs/dev/index.md#openvino-components) work and describes [building](./docs/dev/build.md) and [contributing](./CONTRIBUTING.md) processes.
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[Developer documentation](./docs/dev/index.md) focuses on how OpenVINO [components](./docs/dev/index.md#openvino-components) work and describes [building](./docs/dev/build.md) and [contributing](./CONTRIBUTING.md) processes.
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## Contribution and Support
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docs/dev/index.md

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auto(["AUTO"])
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cpu(["Intel_CPU"])
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gpu(["Intel_GPU"])
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npu(["Intel_NPU"])
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classDef daisy3 fill:#EDB200, stroke: #C98F00, color: #262626
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class auto,cpu,gpu daisy3
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class auto,cpu,gpu,npu daisy3
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end
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subgraph frontends [OV Frontends]
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ir_fe["IR Frontend"]
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</code>
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</pre>
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src\core\README.md
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### OpenVINO Components
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OpenVINO Components include:
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* [OpenVINO™ Runtime](https://docs.openvino.ai/2024/openvino-workflow/running-inference.html) - is a set of C++ libraries with C and Python bindings providing a common API to deliver inference solutions on the platform of your choice.
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* [core](./src/core) - provides the base API for model representation and modification.
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* [inference](./src/inference) - provides an API to infer models on the device.
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* [transformations](./src/common/transformations) - contains the set of common transformations which are used in OpenVINO plugins.
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* [low precision transformations](./src/common/low_precision_transformations) - contains the set of transformations that are used in low precision models
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* [bindings](./src/bindings) - contains all available OpenVINO bindings which are maintained by the OpenVINO team.
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* [c](./src/bindings/c) - C API for OpenVINO™ Runtime
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* [python](./src/bindings/python) - Python API for OpenVINO™ Runtime
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* [Plugins](./src/plugins) - contains OpenVINO plugins which are maintained in open-source by the OpenVINO team. For more information, take a look at the [list of supported devices](#supported-hardware-matrix).
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* [Frontends](./src/frontends) - contains available OpenVINO frontends that allow reading models from the native framework format.
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* [core](../../src/core) - provides the base API for model representation and modification.
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* [inference](../../src/inference) - provides an API to infer models on the device.
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* [transformations](../../src/common/transformations) - contains the set of common transformations which are used in OpenVINO plugins.
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* [low precision transformations](../../src/common/low_precision_transformations) - contains the set of transformations that are used in low precision models
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* [bindings](../../src/bindings) - contains all available OpenVINO bindings which are maintained by the OpenVINO team.
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* [c](../../src/bindings/c) - C API for OpenVINO™ Runtime
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* [python](../../src/bindings/python) - Python API for OpenVINO™ Runtime
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* [Plugins](../../src/plugins) - contains OpenVINO plugins which are maintained in open-source by the OpenVINO team. For more information, take a look at the [list of supported devices](https://docs.openvino.ai/2024/about-openvino/compatibility-and-support/supported-devices.html).
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* [Frontends](../../src/frontends) - contains available OpenVINO frontends that allow reading models from the native framework format.
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* [OpenVINO Model Converter (OVC)](https://docs.openvino.ai/2024/openvino-workflow/model-preparation.html) - is a cross-platform command-line tool that facilitates the transition between training and deployment environments, and adjusts deep learning models for optimal execution on end-point target devices.
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* [Samples](https://github.com/openvinotoolkit/openvino/tree/master/samples) - applications in C, C++ and Python languages that show basic OpenVINO use cases.
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