Deep Learning for Face Detection, Recognition & Aging Udemy Coupon ED Face Detection and Tracking, MTCNN, Face Recognition, FaceNet, Face Aging, GANs NEW Getting Started Udemy Course What you'll learn To understand the architecture of deep learning models used for face detection, recognition, and aging To implement deep learning models used for face detection, recognition, and aging Have the basic knowledge about CNN Familiar with Python programming This is in-depth short course that deeply overviews and explains the architecture of deep learning models that were proposed in the literature for face detection, recognition, and aging. In addition, this course presents the detailed steps for Python code that used to build these deep models. All the development tools and materials required for this course are FREE. Besides that, all implemented Python codes are attached with this course. Who this course is for: Individuals who are interested to know the architecture and implementation of deep learning models used for face detection, recognition, and aging.

VGG is an acronym for the Visual Geometric Group from Oxford University and VGG-16 is a network with 16 layers proposed by the Visual Geometric Group. These 16 layers contain the trainable parameters and there are other layers also like the Max pool layer but those do not contain any trainable parameters. This architecture was the 1st runner up of the Visual Recognition Challenge of 2014 i.e. ILSVRC-2014 and was developed by Simonyan and Zisserman. The VGG research group released a series of the convolution network model starting from VGG11 to VGG19.

This very old neural network architecture was developed in 1998 by a French-American computer scientist Yann André LeCun, Leon Bottou, Yoshua Bengio, and Patrick Haffner. This architecture was developed for the recognition of handwritten and machine-printed characters. It is the basis of other deep learning models. The architecture consists of a total of 7 layers consisting- 2 sets of Convolution layers and 2 sets of Average pooling layers which are followed by a flattening convolution layer. After that, we have 2 dense fully connected layers and finally a softmax classifier.