track = dict(
    author_username='ryanholbrook',
    course_name='computer_vision',
    course_url='https://www.kaggle.com/ryanholbrook/computer-vision'
)

lessons = [
        dict(
            # By convention, this should be a lowercase noun-phrase.
            topic='Testing',
            ),
]

notebooks = [
    dict(
        filename='test.ipynb',
        lesson_idx=0,
        type='exercise',
        scriptid=1,
        ),
]

for nb in notebooks:
    nb['dataset_sources'] = ["ryanholbrook/stanford-cars-for-learn"]

track = dict(
    author_username='ryanholbrook',
    course_name='Computer Vision',
    course_url='https://www.kaggle.com/ryanholbrook/computer-vision'
)

lessons = [
    {'topic': topic_name} for topic_name in
    [
        'The Convolutional Classifier',
        'Convnet Architecture',
        'Filter, Detect, Condense',
        # 'Convolution and Pooling',
        # 'Exploring Convnets',
        # 'Transfer Learning',
        # 'Data Augmentation',
     ]
]

notebooks = [
    dict(
        filename='tut1.ipynb',
        lesson_idx=0,
        type='tutorial',
    ),
    dict(
        filename='tut2.ipynb',
        lesson_idx=1,
        type='tutorial',
    ),
    dict(
        filename='tut3.ipynb',
        lesson_idx=2,
        type='tutorial',
    ),
]

for nb in notebooks:
    nb['dataset_sources'] = [
        "ryanholbrook/stanford-cars-for-learn",
        "ryanholbrook/saved-models",
    ]

Add tracking for lessons 1, 2, 3
Add tracking for lessons 1, 2, 3

  track = dict(
      author_username='ryanholbrook',
-     course_name='computer_vision',
+     course_name='Computer Vision',
      course_url='https://www.kaggle.com/ryanholbrook/computer-vision'
  )
  
  lessons = [
-         dict(
-             # By convention, this should be a lowercase noun-phrase.
-             topic='Testing',
-             ),
+     {'topic': topic_name} for topic_name in
+     [
+         'The Convolutional Classifier',
+         'Convnet Architecture',
+         'Filter, Detect, Condense',
+         # 'Convolution and Pooling',
+         # 'Exploring Convnets',
+         # 'Transfer Learning',
+         # 'Data Augmentation',
+      ]
  ]
  
  notebooks = [
      dict(
-         filename='test.ipynb',
+         filename='tut1.ipynb',
          lesson_idx=0,
-         type='exercise',
+         type='tutorial',
-         scriptid=1,
-         ),
+     ),
+     dict(
+         filename='tut2.ipynb',
+         lesson_idx=1,
+         type='tutorial',
+     ),
+     dict(
+         filename='tut3.ipynb',
+         lesson_idx=2,
+         type='tutorial',
+     ),
  ]
  
  for nb in notebooks:
-     nb['dataset_sources'] = ["ryanholbrook/stanford-cars-for-learn"]
+     nb['dataset_sources'] = [
+         "ryanholbrook/stanford-cars-for-learn",
+         "ryanholbrook/saved-models",
+     ]
  
Add tracking for lessons 1, 2, 3
## Code Before:
track = dict(
    author_username='ryanholbrook',
    course_name='computer_vision',
    course_url='https://www.kaggle.com/ryanholbrook/computer-vision'
)

lessons = [
        dict(
            # By convention, this should be a lowercase noun-phrase.
            topic='Testing',
            ),
]

notebooks = [
    dict(
        filename='test.ipynb',
        lesson_idx=0,
        type='exercise',
        scriptid=1,
        ),
]

for nb in notebooks:
    nb['dataset_sources'] = ["ryanholbrook/stanford-cars-for-learn"]

## Instruction:
Add tracking for lessons 1, 2, 3
## Code After:
track = dict(
    author_username='ryanholbrook',
    course_name='Computer Vision',
    course_url='https://www.kaggle.com/ryanholbrook/computer-vision'
)

lessons = [
    {'topic': topic_name} for topic_name in
    [
        'The Convolutional Classifier',
        'Convnet Architecture',
        'Filter, Detect, Condense',
        # 'Convolution and Pooling',
        # 'Exploring Convnets',
        # 'Transfer Learning',
        # 'Data Augmentation',
     ]
]

notebooks = [
    dict(
        filename='tut1.ipynb',
        lesson_idx=0,
        type='tutorial',
    ),
    dict(
        filename='tut2.ipynb',
        lesson_idx=1,
        type='tutorial',
    ),
    dict(
        filename='tut3.ipynb',
        lesson_idx=2,
        type='tutorial',
    ),
]

for nb in notebooks:
    nb['dataset_sources'] = [
        "ryanholbrook/stanford-cars-for-learn",
        "ryanholbrook/saved-models",
    ]
