In this blog post, I would like to share some insights into how to think about building and managing Machine Learning teams if you are a manager, and also possibly help you get a job in Machine Learning if you are a job seeker.
Datacast Episode 24: From Actuarial Science to Machine Learning with Mael Fabien
Maël is a Data Scientist at Anasen, a Y-Combinator Startup in Paris where he works on the automation of data exploration and feature extraction for textual data. He majored in Actuarial Science at the University of Lausanne and did a second Master in Data Science at Telecom ParisTech Engineering School. He is also a content writer for several blogs and a freelance Machine Learning instructor for 2 boot camps in Paris and Dakar. He is especially interested in applications of Machine Learning in the medical field.
The 7 Questions You Need to Ask to Operate Deep Learning Infrastructure at Scale
Datacast Episode 23: Machine Learning for Finance with Jannes Klaas
The 5 Steps to Set Your Machine Learning Projects Up for Success
Recommendation System Series Part 3: The 6 Research Directions of Deep Recommendation Systems That Will Change The Game
In this post and those to follow, I will be walking through the creation and training of recommendation systems, as I am currently working on this topic for Master Thesis. Part 3 addresses the limitations of using deep learning-based recommendation models by proposing a couple of research directions that might be relevant for the recommendation system scholar community.
Datacast Episode 22: Leading Self-Driving Cars Projects with Jan Zawadzki
Jan Zawadzki has 6 years of experience at a global consultancy and as a Data Scientist. Jan currently works in the realm of self-driving cars as a Project Lead Data Science for Carmeq GmbH, the innovation vehicle of Volkswagen AG. Jan is passionate about advancing the automotive industry through machine learning and sharing his knowledge in the fields of Business and Data Science. He is a top contributor to the “Towards Data Science” Publication on Medium. He is also a deeplearning.ai Ambassador, supporting the team around Deep Learning Luminary Andrew Ng.
What I Learned From Attending All Tech Is Human NYC 2019
What I Learned from Attending New York City Quantum Summit 2019
Recommendation System Series Part 2: The 10 Categories of Deep Recommendation Systems That Academic Researchers Should Pay Attention To
In this post and those to follow, I will be walking through the creation and training of recommendation systems, as I am currently working on this topic for my Master Thesis. Part 2 provides a nice review of the ongoing research initiatives with regard to the strengths, weaknesses, and application scenarios of these models.