Training a model simply means learning (determining) good values for all the weights and the bias from labeled examples. In supervised learning, a machine learning algorithm builds a model by examining many examples and attempting to find a model that minimizes loss; this process is called empirical risk minimization.
Discover strategies for recruiting and retaining top talent in insurance, including mentorship, community partnerships, flexibility, and strong retention tactics.
When expanding your book of business, we often focus on producers working their networks, building relationships, and capitalizing on opportunities to bring on new accounts. However, what about the internal growth opportunities at your agency?
Most people do not know how fascinatingly intricate the insurance industry is. Our very large industry somehow is a well-kept employment secret. How can we attract people from outside the insurance industry into the insurance industry?
Technology change is not “a necessary evil”; it’s a necessary evolution. SMBs are limited by scale, meaning they have to adopt technology differently to maximize ROI. Being intentional during software procurement not only helps ensure success but reduces staff anxiety.
The CEO now has an especially tough decision - move forward with unreliable data, or miss an important opportunity to grow the company? While this seems like a hypothetical scenario, it's very much real.
The 0.4.0 release of ReFocus brings the ability to train your data as a Recommender System model.
