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Docs spelling and grammar fixes (#13307)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: RainRat <rainrat78@yahoo.ca>
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docs/en/integrations/ray-tune.md
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@ -6,7 +6,7 @@ keywords: Ultralytics, YOLOv8, Ray Tune, hyperparameter tuning, machine learning
# Efficient Hyperparameter Tuning with Ray Tune and YOLOv8
Hyperparameter tuning is vital in achieving peak model performance by discovering the optimal set of hyperparameters. This involves running trials with different hyperparameters and evaluating each trial’s performance.
Hyperparameter tuning is vital in achieving peak model performance by discovering the optimal set of hyperparameters. This involves running trials with different hyperparameters and evaluating each trial's performance.
## Accelerate Tuning with Ultralytics YOLOv8 and Ray Tune
@ -182,4 +182,4 @@ plt.show()
In this documentation, we covered common workflows to analyze the results of experiments run with Ray Tune using Ultralytics. The key steps include loading the experiment results from a directory, performing basic experiment-level and trial-level analysis and plotting metrics.
Explore further by looking into Ray Tune’s [Analyze Results](https://docs.ray.io/en/latest/tune/examples/tune_analyze_results.html) docs page to get the most out of your hyperparameter tuning experiments.
Explore further by looking into Ray Tune's [Analyze Results](https://docs.ray.io/en/latest/tune/examples/tune_analyze_results.html) docs page to get the most out of your hyperparameter tuning experiments.