Merge pull request #207535 from amibp/main

Fixing correctness

Author: PRMerger14

articles/machine-learning/how-to-tune-hyperparameters.md

@@ -94,7 +94,7 @@ command_job_for_sweep = command_job(
 
 This code defines a search space with two parameters - `learning_rate` and `keep_probability`. `learning_rate` has a normal distribution with mean value 10 and a standard deviation of 3. `keep_probability` has a uniform distribution with a minimum value of 0.05 and a maximum value of 0.1.
 
-For the CLI, you can use the [sweep job YAML schema](./reference-yaml-job-sweep.md)., to define the search space in your YAML:
+For the CLI, you can use the [sweep job YAML schema](./reference-yaml-job-sweep.md), to define the search space in your YAML:
 ```YAML
     search_space:
         conv_size:
@@ -319,7 +319,7 @@ sweep_job.early_termination = None
 
 ### Picking an early termination policy
 
-* For a conservative policy that provides savings without terminating promising jobs, consider a Median Stopping Policy with `evaluation_interval` 1 and `delay_evaluation` 5. These are conservative settings, that can provide approximately 25%-35% savings with no loss on primary metric (based on our evaluation data).
+* For a conservative policy that provides savings without terminating promising jobs, consider a Median Stopping Policy with `evaluation_interval` 1 and `delay_evaluation` 5. These are conservative settings that can provide approximately 25%-35% savings with no loss on primary metric (based on our evaluation data).
 * For more aggressive savings, use Bandit Policy with a smaller allowable slack or Truncation Selection Policy with a larger truncation percentage.
 
 ## Set limits for your sweep job
@@ -438,7 +438,7 @@ You can visualize all of your hyperparameter tuning jobs in the [Azure Machine L
 
     :::image type="content" source="media/how-to-tune-hyperparameters/hyperparameter-tuning-metrics.png" alt-text="Hyperparameter tuning metrics chart":::
 
-- **Parallel Coordinates Chart**: This visualization shows the correlation between primary metric performance and individual hyperparameter values. The chart is interactive via movement of axes (click and drag by the axis label), and by highlighting values across a single axis (click and drag vertically along a single axis to highlight a range of desired values). The parallel coordinates chart includes an axis on the right most portion of the chart that plots the best metric value corresponding to the hyperparameters set for that job instance. This axis is provided in order to project the chart gradient legend onto the data in a more readable fashion.
+- **Parallel Coordinates Chart**: This visualization shows the correlation between primary metric performance and individual hyperparameter values. The chart is interactive via movement of axes (click and drag by the axis label), and by highlighting values across a single axis (click and drag vertically along a single axis to highlight a range of desired values). The parallel coordinates chart includes an axis on the rightmost portion of the chart that plots the best metric value corresponding to the hyperparameters set for that job instance. This axis is provided in order to project the chart gradient legend onto the data in a more readable fashion.
 
     :::image type="content" source="media/how-to-tune-hyperparameters/hyperparameter-tuning-parallel-coordinates.png" alt-text="Hyperparameter tuning parallel coordinates chart":::