Merge pull request #296 from mohammad-gh009/patch-5

Delete the duplicate sentence!

chapters/en/unit9/intro_to_model_optimization.mdx

@@ -28,7 +28,7 @@ There are several techniques in the model optimization, which will be explained
 
 ## Trade-offs between accuracy, performance, and resource usage
 A trade-off exists between accuracy, performance, and resource usage when deploying a model. That's when we have to decide which part to prioritize so that the model can be maximized in the case at hand.
-1. Accuracy is the model's ability to predict correctly. High accuracy is needed in all applications, which also causes higher performance and resource usage. Complex models with high accuracy usually require a lot of memory, so there will be limitations if they are deployed on resource-constrained devices. Complex models with high accuracy usually require a lot of memory, so there will be limitations if they are deployed on resource-constrained devices.
+1. Accuracy is the model's ability to predict correctly. High accuracy is needed in all applications, which also causes higher performance and resource usage. Complex models with high accuracy usually require a lot of memory, so there will be limitations if they are deployed on resource-constrained devices.
 2. Performance is the model's speed and efficiency (latency). This is important so the model can make predictions quickly, even in real time. However, optimizing performance will usually result in decreasing accuracy.
 3. Resource usage is the computational resources needed to perform inference on the model, such as CPU, memory, and storage. Efficient resource usage is crucial if we want to deploy models on devices with certain limitations, such as smartphones or IoT devices.