Search results
Results From The WOW.Com Content Network
Project Jupyter's name is a reference to the three core programming languages supported by Jupyter, which are Julia, Python and R. Its name and logo are an homage to Galileo 's discovery of the moons of Jupiter , as documented in notebooks attributed to Galileo.
IPython continued to exist as a Python shell and kernel for Jupyter, but the notebook interface and other language-agnostic parts of IPython were moved under the Jupyter name. [ 11 ] [ 12 ] Jupyter is language agnostic and its name is a reference to core programming languages supported by Jupyter, which are Julia , Python , and R .
According to Stephen Wolfram: "The idea of a notebook is to have an interactive document that freely mixes code, results, graphics, text and everything else.", [4] and according to the Jupyter Project Documentation: "The notebook extends the console-based approach to interactive computing in a qualitatively new direction, providing a web-based ...
An oversimplification of how a kernel connects application software to the hardware of a computer. A kernel is a computer program at the core of a computer's operating system that always has complete control over everything in the system. The kernel is also responsible for preventing and mitigating conflicts between different processes. [1]
A standard example for a kernelization algorithm is the kernelization of the vertex cover problem by S. Buss. [1] In this problem, the input is an undirected graph together with a number .
In machine learning, kernel machines are a class of algorithms for pattern analysis, whose best known member is the support-vector machine (SVM). These methods involve using linear classifiers to solve nonlinear problems. [ 1 ]
Output after kernel PCA, with a Gaussian kernel. Note in particular that the first principal component is enough to distinguish the three different groups, which is impossible using only linear PCA, because linear PCA operates only in the given (in this case two-dimensional) space, in which these concentric point clouds are not linearly separable.
Detailed analysis of the Mach bottleneck indicated that, among other things, its working set is too large: the IPC code expresses poor spatial locality; that is, it results in too many cache misses, of which most are in-kernel. [2] This analysis gave rise to the principle that an efficient microkernel should be small enough that the majority of ...