We cover deep neural network in our curriculum which are the artificial neural networks based on biological neurons they can learn and combine simple features to make a complex function. Like in following they learn to classify between red and blue dots with simple linear boundaries to make complex,
In Deep learning mainly 2 parts came -
Computer vision (CNN) - Image recogniton, object detection, activity
Natural language processing - text classification, language translation,
CNN - does take a biological inspiration from the visual cortex. The visual
cortex has small regions of cells that are sensitive to specific regions of the
visual field, like some neurons fired when exposed to vertical edges and some
when shown horizontal or diagonal edges.
A more detailed overview of what CNN's do would be that you take the image,
pass it through a series of convolutional, non-linear, pooling (downsampling),
and fully connected layers, and get an output.
Don’t be feared from this layers they just feature extractors and make complex
decision combining those simple features.
We cover the Computer vision Or Convolution neural
networks different Topics -
Beside of recognition we give in-depth knowledge on object detection and segmentation networks like YOLO and their different versions.
We are focused on practical working with the theory of this networks we implement then in Keras, tense flow etc . framework to build something which can show you the real world use cases of computer vision.
There are different architectures we cover from the start to the recent state of the networks - SO
SO actually convolution Networks become so popular when then surpass
human-level performance in 2012 Imagenet competition for image
We cover mainly these networks in image recognition task -