Small edits (#162)

docs/fundamentals/agents_and_queues.md

@@ -26,7 +26,7 @@ can allocate several GPUs to an agent and use the rest for a different workload,
 
 ## What is a Queue?
 
-A ClearML queue is an ordered list of Tasks scheduled for execution. A queue can be serviced by one or multiple agents. 
+A ClearML queue is an ordered list of Tasks scheduled for execution. One or multiple agents can service a queue. 
 Agents servicing a queue pull the queued tasks in order and execute them. 
 
 A ClearML Agent can service multiple queues in either of the following modes: 
@@ -51,8 +51,8 @@ The diagram above demonstrates a typical flow where an agent executes a task:
    1.  Set up the python environment and required packages.
 1. The task's script/code is executed.  
 
-While the agent is running, it continuously reports system metrics to the ClearML Server (these can be monitored in the 
-[**Workers and Queues**](../webapp/webapp_workers_queues.md) page).  
+While the agent is running, it continuously reports system metrics to the ClearML Server. You can monitor these metrics
+in the [**Workers and Queues**](../webapp/webapp_workers_queues.md) page.  
 
 ## Resource Management
 Installing an Agent on machines allows it to monitor all the machine's status (GPU / CPU / Memory / Network / Disk IO). 

docs/fundamentals/hpo.md

@@ -6,7 +6,7 @@ title: Hyperparameter Optimization
 Hyperparameters are variables that directly control the behaviors of training algorithms, and have a significant effect on 
 the performance of the resulting machine learning models. Finding the hyperparameter values that yield the best 
 performing models can be complicated. Manually adjusting hyperparameters over the course of many training trials can be 
-slow and tedious. Luckily, hyperparameter optimization can be automated and boosted using ClearML's 
+slow and tedious. Luckily, you can automate and boost hyperparameter optimization with ClearML's 
 [**`HyperParameterOptimizer`**](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class.
 
 ## ClearML's HyperParameter Optimization
@@ -77,11 +77,12 @@ optimization.
   ```python
   from clearml import Task
   
-  task = Task.init(project_name='Hyper-Parameter Optimization',
-                 task_name='Automatic Hyper-Parameter Optimization',
-                 task_type=Task.TaskTypes.optimizer,
-                 reuse_last_task_id=False)
-  
+  task = Task.init(
+      project_name='Hyper-Parameter Optimization',
+      task_name='Automatic Hyper-Parameter Optimization',
+      task_type=Task.TaskTypes.optimizer,
+      reuse_last_task_id=False
+  )
   ```
 
 1. Define the optimization configuration and resources budget:

docs/fundamentals/logger.md

@@ -8,7 +8,7 @@ member of the [Task](task.md) object.
 ClearML integrates with the leading visualization libraries, and automatically captures reports to them. 
 
 ## Types of Logged Results
-In ClearML, there are four types of reports:
+ClearML supports four types of reports:
 - Text - Mostly captured automatically from stdout and stderr but can be logged manually.
 - Scalars - Time series data. X-axis is always a sequential number, usually iterations but can be epochs or others.
 - Plots - General graphs and diagrams, such as histograms, confusion matrices line plots, and custom plotly charts.

docs/fundamentals/task.md

@@ -14,7 +14,7 @@ information as well as execution outputs.
 All the information captured by a task is by default uploaded to the [ClearML Server](../deploying_clearml/clearml_server.md) 
 and it can be visualized in the [ClearML WebApp](../webapp/webapp_overview.md) (UI). ClearML can also be configured to upload 
 model checkpoints, artifacts, and charts to cloud storage (see [Storage](../integrations/storage.md)). Additionally, 
-there is an option to work with tasks in Offline Mode, in which all information is saved in a local folder (see 
+you can work with tasks in Offline Mode, in which all information is saved in a local folder (see 
 [Storing Task Data Offline](../guides/set_offline.md)).
 
 In the UI and code, tasks are grouped into [projects](projects.md), which are logical entities similar to folders. Users can decide