Question

List out the parameters used to find the
efficiency of an Almodel​

Answer 1

Answer:

The parameters of a neural network are typically the weights of the connections. In this case, these parameters are learned during the training stage. So, the algorithm itself (and the input data) tunes these parameters. The hyper parameters are typically the learning rate, the batch size or the number of epochs.

Explanation:

Algorithmic efficiency can be defined as reducing the compute needed to train a specific capability. Efficiency is the primary way we measure algorithmic progress on classic computer science problems like sorting. Efficiency gains on traditional problems like sorting are more straightforward to measure than in ML because they have a clearer measure of task difficulty.[1] However, we can apply the efficiency lens to machine learning by holding performance constant. Efficiency trends can be compared across domains like DNA sequencing17 (10-month doubling), solar energy18 (6-year doubling), and transistor density3 (2-year doubling).

For our analysis, we primarily leveraged open-source re-implementations192021 to measure progress on AlexNet level performance over a long horizon. We saw a similar rate of training efficiency improvement for ResNet-50 level performance on ImageNet (17-month doubling time).716 We saw faster rates of improvement over shorter timescales in Translation, Go, and Dota 2:

Answer 2

Like any digital product, an AI product's success should be determined by its profit contribution. Business performance indicators such as operating cash flow (OCF) or the monthly recurring revenue (MRR) are suitable for measuring the product's contribution to the company's success.