* Learning about essential components of the Perception package and creating a basic simulation with these essential elements.
* Running your simulations on your computer and observing real-time visualizations of the Perception tools working.
* Finding the synthetic data generated from your simulation and understanding its various pieces
* Generating common statistics for your synthetic data (e.g. number of objects in each frame, presence of each object in the whole data, etc.)
* Generating common statistics and visualizations for your synthetic data (e.g. number of objects in each frame, presence of each object in the whole data, etc.)
In order to get the best out of most perception-oriented machine learning models, the training data needs to contain a large-degree of variation. As a general rule of thumb, the more varied data you can feed to a model while training, the better it performs. This is achieved through randomizing various aspects of your simulation between frames. While you will use basic randomizations in Phase 1, **Phase 2** of the tutorial will help you learn how to randomize your simulations in more complex ways by guiding you through writing your first customized Randomizer in C#. This phase is called **Custom and Complex Randomizations**
In order to get the best out of most perception-oriented machine learning models, the training data needs to contain a large-degree of variation. As a general rule of thumb, the more varied data you can feed to a model while training, the better it performs. This is achieved through randomizing various aspects of your simulation between frames. While you will use basic randomizations in Phase 1.
**Phase 2** of the tutorial will help you learn how to randomize your simulations in more complex ways by guiding you through writing your first customized randomizer in C#. This phase is called **Custom and Complex Randomizations**, and once you complete it, you will know how to:
* Create custom randomizers by extending our provided samples
* Coordinate the operation of several randomizers by specifying their order of execution and the objects they affect
* Have objects specify criteria (e.g. ranges, means, etc.) for their randomizable attributes.
Finaly, in **Phase 3**, which is simply named **Cloud**, you will learn how to:
* Generate larger-scale synthetic datasets by leveraging the power of Unity Simulation.
* Download the cloud-generated data
* Generate common and custom statistics and visualizations for your cloud-generated data.
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