Gradient Works Pricing Overview

Provides the team with more even distribution and being able to attain quotas;

Enables "focus" to be on appropriate accounts;

Eases the Scaling Process 1000%;

Allows for quick response to market conditions changes - makimg them more adaptable; and

Transforms sales efficiency. Review collected by and hosted on G2.com.

The cons revolve around the gradient decent optimizatiom algorithms that are used in machine learning. These inclde: getting stuck in local mininma; learning rate sensitivity (learning at a "too high" rate causes the algorithm to overshoot minimums and "too low" a rate leads to slow convergence; slow convergence due to the algorithm taking a large number of iterations to develop a solution which makes it computationally expensive; in deep neural networks, gradients can vanish when gradients become very small or explode during backpropagation, hindering learning in certain layers; overfitting when the model is trained for too long with a high learning rate, causing risk of it learning the training data too closely, leading to poor performance on unseen data; large datasets can be computationally intensive when calculating gradients, again, raising computational cost; and it provides limited interpreterability in understanding the exact relationship between features and predictions. Review collected by and hosted on G2.com.