norm
There are 0 repository under norm topic.
adamnemecek / adjoint
Thoughts on adjoint, norm and such.
- Language:Shell 41
- Language:Nim 30
PhilippMDoerner / Snorlogue
An plugin for the prologue web-framework of the nim-language. Provides an admin interface
Language:Nim 25- Language:C++ 24
blaisedelattre / lip4conv
Code for Spectral Norm of Convolutional Layers with Circular and Zero Paddings and Efficient Bound of Lipschitz Constant for Convolutional Layers by Gram Iteration
Language:Python 10slimdestro / gourlnormalizer
url normalizer[RFC 3986]-Golang
Language:Go 7cyrilcolinet / Epitech-HEADER-2018
:zap: Epitech header generator for atom editor.
Language:CoffeeScript 3- Language:Nim 3
rayyamhk / Matrix.js
A professional, comprehensive and high-performance library for you to manipulate matrices.
Language:JavaScript 3- Language:Python 3
drilonaliu / Numerical-Analysis-2
A package for numerical analysis with matrices, norms, finding eigenvalues and eigenvectors, and iterative methods for solving system of linear equations.
Language:Java 2shinshiner / Gene_Chip_Ananysis
Classification on gene chips
Language:TeX 2stdlib-js / blas-base-dasum
Compute the sum of absolute values (L1 norm).
Language:Python 2Compute the sum of absolute values (L1 norm).
Language:JavaScript 2Compute the sum of absolute values (L1 norm).
Language:Python 2Calculate the L2-norm of a single-precision floating-point vector.
Language:Python 2Calculate the sum of absolute values (L1 norm) of double-precision floating-point strided array elements, ignoring NaN values and using ordinary recursive summation.
Language:JavaScript 2- NORMLanguage:PHP 2
vaitybharati / Assignment-03-Q3-Hypothesis-Testing-
Chi2 contengency independence test. Assume Null Hypothesis as Ho: Independence of categorical variables (male-female buyer rations are similar across regions (does not vary and are not related) Thus Alternate Hypothesis as Ha: Dependence of categorical variables (male-female buyer rations are NOT similar across regions (does vary and somewhat/significantly related)
Language:Jupyter Notebook 2Q 22) Calculate the Z scores of 90% confidence interval,94% confidence interval, 60% confidence interval for Adipose Tissue (AT) and Waist Circumference(Waist) from wc-at data set
Language:Jupyter Notebook 2- Language:Nim 1
Calculate the L2-norm of a double-precision floating-point vector.
Language:Python 1Calculate the L2-norm of a vector.
Language:JavaScript 1Calculate the sum of absolute values (L1 norm) of double-precision floating-point strided array elements using pairwise summation.
Language:JavaScript 1Calculate the sum of absolute values (L1 norm) of double-precision floating-point strided array elements, ignoring NaN values.
Language:JavaScript 1Calculate the sum of absolute values (L1 norm) of strided array elements using pairwise summation.
Language:JavaScript 1Calculate the sum of absolute values (L1 norm) of single-precision floating-point strided array elements using pairwise summation.
Language:JavaScript 1Chi2 contengency independence test. Q4. TeleCall uses 4 centers around the globe to process customer order forms. They audit a certain % of the customer order forms. Any error in order form renders it defective and has to be reworked before processing. The manager wants to check whether the defective % varies by centre. Please analyze the data at 5% significance level and help the manager draw appropriate inferences.
Language:Jupyter Notebook 1Suppose GMAT scores can be reasonably modeled using a normal distribution with mean=711 and SD = 29. What is P(X<=680) What is P(697<=X<=740)
Language:Jupyter Notebook 1To understand Normal Distribution and its application. Daily returns of stocks traded in BSE (Bombay Stock Exchange). To understand risk and returns associated with various stocks before investing in them. BEML and GLAXO Stocks study.
Language:Jupyter Notebook 1credit card launch example sample mean: 1990 sample SD: 2833 Pop SD: 2500 Pop mean: ? n=140 Q: Construct 95% confidence interval for mean card balance and interpret it
Language:Jupyter Notebook 1Find confidence intervals for Beml and Glaxo stocks. Confidence Interval Estimate
Language:Jupyter Notebook 1Hypothesis-Testing 1S1T-Super-Market-Loyality-Program. Population Parameters: Mean=120 Sample Parameters: n=80, Mean=130, SD=40, df=80-1=79
Language:Jupyter Notebook 1Hypothesis Testing 1S2T - Call Center Process. Sample Parameters: n=50, df=50-1=49, Mean1=4, SD1=3 1-sample 2-tail ttest Assume Null Hypothesis Ho as Mean1 = 4 Thus, Alternate Hypothesis Ha as Mean1 ≠ 4
Language:Jupyter Notebook 1
