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Author: czoido

_posts/2024-12-20-You-can-do-AI-with-cpp.markdown

@@ -131,77 +131,71 @@ can be easily integrated into your code. For more information on Tensorflow Lite
 #### ONNX Runtime
 
 **ONNX Runtime** is a high-performance inference engine designed to run models in the
-[ONNX](https://onnx.ai/) format, an open standard that facilitates representing and
-transferring neural network models across various AI frameworks such as PyTorch,
-TensorFlow, or scikit-learn.
+[ONNX](https://onnx.ai/) format, an open standard for representing network models across
+various AI frameworks such as PyTorch, TensorFlow, and scikit-learn.
 
-Thanks to this interoperability, you can run models trained in multiple frameworks using a
-single unified runtime. The general idea is:
+Thanks to this interoperability, ONNX Runtime allows you to use models trained in
+different frameworks with a single unified runtime. Here’s the general workflow:
 
-1. **Get a model**: Train it using your preferred framework and export or convert it to
-   the ONNX format. There are [tutorials](https://onnxruntime.ai/docs/tutorials/) showing
-   how to do this for popular frameworks and libraries.
+1. **Get a model**: Train a model using your preferred framework and export or convert it
+   to the ONNX format. There are [tutorials](https://onnxruntime.ai/docs/tutorials/)
+   available for popular frameworks and libraries.
 
 2. **Load and run the model with ONNX Runtime**: Check out these [C++ inference
    examples](https://github.com/microsoft/onnxruntime-inference-examples/tree/main/c_cxx)
-   to quickly get started with some code samples. 
-   
-From there, ONNX Runtime offers options to tune performance using various runtime
-configurations or hardware accelerators. There are many possibilities—check [the
-Performance section in the documentation](https://onnxruntime.ai/docs/performance/) for a
-more in-depth look.
+   to get started quickly.
 
-ONNX Runtime’s flexibility allows you to experiment with models from diverse sources,
-integrate them into your C++ applications, and scale as needed. For more details, check
-out the [ONNX Runtime documentation](https://onnxruntime.ai/docs/).
+Additionally, ONNX Runtime offers multiple options for tuning performance using various
+runtime configurations or hardware accelerators. Explore [the Performance section in the
+documentation](https://onnxruntime.ai/docs/performance/) for more details. For more
+information, visit the [ONNX Runtime documentation](https://onnxruntime.ai/docs/).
 
 #### OpenVINO
 
 **OpenVINO** (Open Visual Inference and Neural Network Optimization) is an
 [Intel-developed toolkit](https://docs.openvino.ai/) that accelerates deep learning
-inference across a range of devices. It supports models from popular frameworks like
-PyTorch, TensorFlow, and ONNX, offering tools to optimize, deploy, and scale AI
-applications efficiently.
+inference on a wide range of devices. It supports models from frameworks like PyTorch,
+TensorFlow, and ONNX, offering tools to optimize, deploy, and scale AI applications
+efficiently.
 
-You can check some of their [C++
-examples](https://docs.openvino.ai/2024/learn-openvino/openvino-samples.html)
-demonstrating tasks like model loading, inference, and performance benchmarking, to help
-you get started.
+The [OpenVINO C++
+examples](https://docs.openvino.ai/2024/learn-openvino/openvino-samples.html) demonstrate
+tasks such as model loading, inference, and performance benchmarking. Explore these
+examples to see how you can integrate OpenVINO into your projects.
 
 For more details, visit the [OpenVINO documentation](https://docs.openvino.ai/2024/).
 
 #### mlpack
 
-**mlpack** is a fast and flexible header-only C++ library for machine learning, designed
-for both lightweight deployment and interactive prototyping via tools like C++ notebooks.
-It offers a broad range of algorithms for classification, regression, clustering, and
-more, along with preprocessing utilities and transformations.
+**mlpack** is a fast, flexible, and lightweight header-only C++ library for machine
+learning. It is ideal for lightweight deployments and prototyping. It offers a broad range
+of machine learning algorithms for classification, regression, clustering, and more, along
+with preprocessing utilities and data transformations.
 
-To explore [mlpack](https://www.mlpack.org/), visit the [examples
-repository](https://github.com/mlpack/examples/tree/master/cpp), which showcases C++
-applications like training neural networks for digit recognition, using decision trees to
-predict loan defaults, and applying clustering to find patterns in healthcare datasets.
+Explore [mlpack’s examples
+repository](https://github.com/mlpack/examples/tree/master/cpp), where you’ll find C++
+applications such as training neural networks for digit recognition, decision tree models
+for predicting loan defaults, and clustering algorithms for identifying patterns in
+healthcare data.
 
-For more details, visit the [mlpack documentation](https://www.mlpack.org/).
+For further details, visit the [mlpack documentation](https://www.mlpack.org/).
 
-### Dlib
+#### Dlib
 
-**Dlib** is a modern C++ library offering advanced machine learning algorithms and
-computer vision functionalities, widely adopted in research and industry. Its
-well-designed API and comprehensive documentation make it easy to integrate ML
-capabilities into existing projects.
+**Dlib** is a modern C++ library widely used in research and industry for advanced machine
+learning algorithms and computer vision tasks. Its comprehensive documentation and
+well-designed API make it straightforward to integrate into existing projects.
 
-It provides algorithms for facial detection, landmark recognition, object classification,
-and tracking. Examples showcasing these algorithms can be found in [their GitHub
-repository](https://github.com/davisking/dlib/tree/master/examples). For more details,
-visit the [Dlib official site](http://dlib.net/).
+Dlib provides a variety of algorithms, including facial detection, landmark recognition,
+object classification, and tracking. Examples of these functionalities can be found in
+[their GitHub repository](https://github.com/davisking/dlib/tree/master/examples). 
+
+For more information, visit the [Dlib official site](http://dlib.net/).
 
 ## Conclusion
 
-There is a wide variety of libraries available in C++ for working with AI. An additional
-advantage is the ability to customize optimizations for different platforms, enabling
-faster and more energy-efficient AI workflows. With Conan, integrating these libraries
-into your projects is both straightforward and flexible.
+C++ offers high-performance AI libraries and the flexibility to optimize for your
+hardware. With Conan, integrating these tools is straightforward, enabling efficient,
+scalable AI workflows.
 
-With C++ and these libraries, getting started with AI is easier than you think. Give them
-a try and see what you can build!
+Now, give these tools a go and see your AI ideas come to life in C++!