In this blog post, I would like to share the 5 steps that you can use to set up 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.
Datacast Episode 21: How To Be a Data Science Writer with Admond Lee
Admond is currently a Data Scientist at Micron focusing on Smart Manufacturing and AI. With his degree in Physics, Admond discovered and pursued his passion in data science and never looked back ever since. He is also an independent data scientist and consultant helping companies and digital marketing agencies achieve their marketing ROI using advanced social analytics and machine learning. Being a passionate writer who writes about data science, his data science work and experience have been featured by various publications, including KDnuggets, Medium, Tech in Asia, AI Time Journal and local business magazine.
Recommendation System Series Part 1: An Executive Guide to Building Recommendation System
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 1 provides a high-level overview of recommendation systems, how they are built, and how they can be used to improve businesses across industries.
A Friendly Introduction to Data-Driven Marketing for Business Leaders
How to Run an Effective Data Science POC in 7 Steps
What does running a POC mean in practice specifically for data science? When it comes to the evaluation of data science solutions, POCs should prove not just that a solution solves one particular, specific problem, but that a system will provide widespread value to the company: that it’s capable of bringing a data-driven perspective to a range of the business’s strategic objectives.