Deeplearning.ai Convolutional Neural Networks Certification

Although computer vision is not my domain, and after a lot of hard work and with a hectic schedule, I finally completed #deplearningai 's specialization course on #convolutionalneuralnetworks . With this, I only have one course remaining to complete the specialization. I must say that I found it particularly challenging compared to the three previous ones, but just as interesting! Indeed, now I have a solid theoretical and some practical foundations on:

- Basic #CNN concepts: grayscale and RGB image representation, 2D and 3D #convolutions, filters, striding and padding.
- CNN #architecturedesign : convolutional, pooling, and fully-connected layers, skip connections, 1x1 convolutions, deep conv blocks.
- Important and state-of-the-art image CNN architectures: LeNet-5#AlexNett, #vgg16 , #resnet , #inception , and more.
#transferlearning and data augmentation techniques.
#objectdetection and #landmark detection: convolutional sliding windows, bounding boxes, IoU, non-max suppression and anchor boxes.
#YOLO and #YOLO9000 object detection algorithms.
- Face verification and #facerecognition : one-shot problems, #siamese networks, triplet loss.
- Neural style transfer

Next steps: implementing object detection on my laptop, and reading the papers in-full!





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