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guide/14-deep-learning/how_AutoDL_works.ipynb

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"`AutoDL` class accepts the following paramters:\n",
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"\n",
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"- `data` (Required Paramter): Returned data object from `prepare_data` function in the previous step.\n",
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"- `data` (Required Parameter): The data object returned from the `prepare_data` function in the previous step.\n",
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" \n",
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"- `total_time_limit` (Optional parameter):\n",
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" The total time limit in hours for `AutoDL` to train and evaluate the networks. This parameter becomes important when time is the main constraint to the user. The `AutoDL` class calculates the number of chips that can be processed in the given time from the prepared databunch.\n",
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"- `total_time_limit` (Optional parameter): The total time limit in hours for the `AutoDL` to train and evaluate the networks. This parameter becomes important when time is the main constraint to the user. The `AutoDL` class calculates the number of chips that can be processed in the given time from the prepared databunch.\n",
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" \n",
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"- `mode` (Optional Parameter): Can be \"basic\" or \"advanced\".\n",
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" - _basic_ : To to be used when the user wants to train all selected networks.\n",
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" - _advanced_ : To be used when the user additionally wants to tune hyper parameters of two best performing models from basic mode.\n",
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" - _advanced_ : To be used when the user also wants to tune hyper parameters of the two best performing models from the basic mode.\n",
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"- `networks` (Optional Parameter):\n",
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" The list of models that will be used in the training process. If the user does not provides the parameter value the `AutoDL` class selects all the supported networks, however user can select one or multiple networks by passing the network name in a list.\n",
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" The list of models that will be used in the training process. If the user does not provide the parameter value, the `AutoDL` class selects all of the supported networks, however the user can select one or more networks by passing the network names in a list.\n",
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"\n",
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" - _Supported Object Detection models_\n",
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" - SingleShotDetector, RetinaNet, FasterRCNN, YOLOv3, MMDetection\n",

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