Target audience: Anyone interested in Deep learning.
Target audience: Anyone interested in Deep learning.
Previous experience: Background in Linear algebra, Calculus, python programming and Machine learning is required
Course type: Theoretical lectures combined with hands-on experience
Machine learning is a rapidly growing field in Computer science, to the extent that it became a very popular buzz word. It seems it is everywhere today – from self driving cars to automatic cancerous tumours detection. Deep learning is a sub field in the world of Machine learning mainly based around neural networks – a conceptual model of the human brain that has been around for decades but is getting more and more attention in the last several years. Using this model we are capable of achieving wonderful results in solving complex problems that were once out of our reach. In this course we will start our journey in the world of deep learning – we will start by getting familiar with basic concepts and theory, all the way down to actual hands-on practice. We will cover important topics such as Convolutional neural networks (Convolution, Correlation, and Filtering), Generative Adversarial Networks, Deep reinforcement learning, common tools and much more.