Convolutional neural networks (CNNs) enable very powerful deep learning based techniques for processing, generating, and sense making of visual information. These are revolutionary techniques in computer vision that impact technologies ranging from e-commerce to self-driving cars.

This session offers an in-depth examination of CNNs, their fundamental processes, their applications, and their role in visualisation and image enhancement.

This session covers concepts, processes, and technologies such as CNN layers and architectures (LeNet, AlexNet, ZF-Net, VGG, GoogleNet & Resnet)
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I will also explains CNN image classification and segmentation, deep dream and style transfer, super-resolution, and generative adversarial networks (GANs).

Learners who come to this session with a basic knowledge of deep learning principles, some computer vision experience, and exposure to engineering math should gain the ability to implement CNNs and use them to create their own visualisations.

Session summary:
* Discover the connections between CNNs and the biological principles of vision
* Understand the advantages and trade-offs of various CNN architectures
* Survey the history and evolution of CNN's on-going development
* Learn to apply the latest GAN, style transfer, and semantic segmentation techniques
* Explore CNN applications, visualisation, and image enhancement