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Note
This is my personal summary after studying the course, convolutional neural networks, which belongs to Deep Learning Specialization. and the copyright belongs to deeplearning.ai.
My personal notes
1st week: 01_foundations-of-convolutional-neural-networks
- 01_computer-vision
- 02_edge-detection-example
- 03_more-edge-detection
- 04_padding
- 05_strided-convolutions
- 06_convolutions-over-volume
- 07_one-layer-of-a-convolutional-network
- 08_simple-convolutional-network-example
- 09_pooling-layers
- 10_cnn-example
- 11_why-convolutions
2nd week: 02_deep-convolutional-models-case-studies
3rd week: 03_object-detection
- 01_object-localization
- 02_landmark-detection
- 03_object-detection
- 04_convolutional-implementation-of-sliding-windows
- 05_bounding-box-predictions
- 06_intersection-over-union
- 07_non-max-suppression
- 08_anchor-boxes
- 09_yolo-algorithm
- 10_optional-region-proposals
4th week: 04_special-applications-face-recognition-neural-style-transfer
My personal programming assignments
1st week: Convolution model Step by Step
2nd week: Keras Tutorial Happy House, Residual Networks
3rd week: Autonomous driving - Car detection
4th week: Deep Learning & Art Neural Style Transfer, Face Recognition for the Happy House