C4W1L01 Computer Vision C4W1L02 Edge Detection Examples C4W1L03 More Edge Detection C4W1L04 Padding C4W1L05 Strided Convolutions C4W1L06 Convolutions Over Volumes C4W1L07 One Layer of a Convolutional Net C4W1L08 Simple Convolutional Network Example C4W1L09 Pooling Layers C4W1L10 CNN Example C4W1L11 Why Convolutions C4W2L01 Why look at case studies? C4W2L02 Classic Network C4W2L03 Resnets C4W2L04 Why ResNets Work C4W2L05 Network In Network C4W2L06 Inception Network Motivation C4W2L07 Inception Network C4W2L08 Using Open Source Implementation C4W2L09 Transfer Learning C4W2L10 Data Augmentation C4W2L11 State of Computer Vision C4W3L01 Object Localization C4W3L02 Landmark Detection C4W3L03 Object Detection C4W3L04 Convolutional Implementation Sliding Windows C4W3L06 Intersection Over Union C4W3L07 Nonmax Suppression C4W3L08 Anchor Boxes C4W3L09 YOLO Algorithm C4W3L10 Region Proposals C4W4L01 What is face recognition C4W4L02 One Shot Learning C4W4L03 Siamese Network C4W4L04 Triplet loss C4W4L05 Face Verification C4W4L06 What is neural style transfer? C4W4L07 What are deep CNs learning? C4W4L08 Cost Function C4W4L09 Content Cost Function C4W4L10 Style Cost Function C4W4L11 1D and 3D Generalizations