Examples of using The algorithm in English and their translations into Chinese
{-}
-
Political
-
Ecclesiastic
-
Programming
As the algorithm is recursive, we can build Bezier curves of any order, that is: using 5, 6 or more control points.
The algorithm was first published by Yefim Dinitz in 1970 and independently published by Jack Edmonds and Richard Karp in 1972.
This time, the algorithm reported that men's brains were 2.4 years older than their true ages.
The algorithm starts by placing each vertex in a cluster of its own: v0, v1, and so on.
For some versions of the algorithm, it is possible to prove that it is convergent(i.e., it is able to find the global optimum in finite time).
Sohn says the next step is to test and calibrate the algorithm on larger, more diverse datasets from different hospitals and countries.
Once the algorithm has been run and the groups are defined, any new data can be easily assigned to the correct group.
This time, according to them, the algorithm reported that men's brains were 2.4 years older than their true ages.
Then the algorithm tries to build a model on its own that can accurately tag a picture of a human and a cat.
In fact, Deep Knowledge itself shifted focus and no longer uses the algorithm.
However, once the algorithm is trained, it can perform“inference” locally- looking at one object to determine if it is a pedestrian.
The algorithm also told me what percentage of text should be dialog and how much of that dialog should come from female characters.
Deep learning allows the algorithm to“teach itself” what to look for by spotting subtle differences among the thousands of images.
K-means: A more primal but very easy-to-understand the algorithm that can be perfect as a baseline in a variety of problems.
So, just as the algorithm can teach itself how to play chess, it can teach itself what product to recommend next online.
The algorithm will gnaw through massive amounts of constantly updated data, learning to discern patterns of“disturbances in the force”.
If the algorithm correctly labels the images, we might conclude that the underlying deep neural network has learned to distinguish cats and dogs.
First, the algorithm predicts the question being heard from a known set of questions, such as“What do you spread on a field?”.