Applied deep learning for image processing

Applying deep learning to image processing helps develop solutions for specific needs such as road sign detection, the detection of faulty parts, disease detection and anomaly detection in electrical equipment. This approach uses a convolutional neural network (CNN) inspired by the human brain. A CNN consists of dozens, sometimes hundreds, of neural layers. Each layer collects and processes data from the previous one. The system can therefore learn to identify characters, words, shapes and faces, among other objects, and classify them.

In this level 2 training programme, you will acquire the skills needed to use deep learning methods effectively for image interpretation and analysis. You will begin by exploring application areas in detail, followed by a module on convolutional neural networks. You will then examine generative methods using autoencoders and GANs, before concluding with techniques for training deep networks.

By the end of this 3-day course, you will have a sound understanding of how deep learning models work and how to use them. You will also be able to modify their architectures to meet specific imaging needs.