How to guarantee your machine learning model will fail on first contact with the real world.
Recently I had my PhD thesis rejected. As a failure, I am uniquely positioned to recognize failure. While I am unwaveringly enthusiastic about machine learning, I aim to share my insights into failed machine learning modelling from real-world examples in science and industry. This talk is for you if you have an introductory understanding of machine learning and would like to avoid common pitfalls.
Jesper is a recovering geophysicist with a knack for coding, machine learning, and data science. During their PhD, Jesper focused on deep learning applied to geophysical data. During this time they finished 10 peer-reviewed papers in workshops, conferences, and journals. Being ever so distractable they also reaching top 81 worldwide in the Kaggle notebooks division and worked as a consultant teaching Python and machine learning in a few places. Recently they finished a book chapter, which is under review, titled “70 Years of Machine Learning in Geoscience in Review”. They were invited to rewrite the O’Reilly teaching material for Tensorflow 2 and have just published a data science with Python video course.