Highly Interactive Introduction To Neural Networks




Highly Interactive Introduction to Neural Networks

7 March 2019

New York

Added 01-Jan-1970

Consultants, scientists, and developers have the tendency to treat neural networks like a magic black box. For those that want a free workshop and course on the understanding of neural networks and how to program this from scratch- this is for you.

Magnus, who combined his extensive experience as an Educational Technology Researcher with a previous Masters in Machine Learning, is obsessed with educational technology and how to create the most optimal understanding of the most complex concepts by breaking them down into their constituent pieces and forming intuitive visualizations with project based learning. He was formerly a researcher at Matric.no (Centre for Research, Innovation and Coordination of Mathematics Teaching in 2016) in the field of educational technology in programming and later the coordinator of a research team that developed an e-learning website that teaches machine learning and optimization. Finally, in 2017 the Research Council awarded sponsorship to evolve the platform further to allow for personalization of education with advanced machine learning algorithms that provide guided feedback to each users specific mistake. The training you receive provides you with a never before seen integration of visual linear algebra, calculus, and project based coding to acheive a 360 degree understanding of the advanced complexities of understanding a neural network.

Proceeding their introductory presentation to neural networks you will receive an amazing interactive tutorial through our platform www.diggit.no, that will take you through how to program an introductory neural network.

Who should attend?
Our neural network course was developed for developers, consultants, data analysts, data scientists or curious learners from this field seeking a better understanding and knowledge of basic principles of neural networks and how to program them.

Course objectives
Gain a practical understanding of the feedforward algorithm, how they work and how they can be applied. Learn python from scratch, data architecture of a neural network, and the general underlying theory.
Be able to build a simple aspect of a neural network and receive a course as to how you can fully build a neural network from scratch.