• ## Mean filtering

Time：2021-9-17

Mean filtering The pixel value of any point is the average of the surrounding n x N pixel valuesExample: new pixel value of red dot = sum of pixels in blue background area divided by 25New pixel values for red dots=（（197 + 25 +106 +156 +159）+（149 + 40 + 107 + 5 + 71) + […]

• ## Multivariate analysis of variance in Statistical Science

Time：2021-7-24

01. Preface We talked about simple one-way ANOVA before. In this article, we talk about two-way ANOVA and multi-factor ANOVA. Two-way ANOVA is the simplest multi-factor ANOVA. Single factor analysis is to consider that only one factor will affect the mean value to be compared, while multi factor analysis is that multiple factors will affect […]

• ## On the theorem of large numbers

Time：2021-7-20

We talked about the central limit theorem. In this section, we will talk about the theorem of large numbers. The theorem of large numbers and the central limit theorem are relatively close concepts, and these two theorems often appear together. Let’s take a concrete look at the content of the theorem of large numbers The […]

• ## Is the mean and expectation in statistical science the same thing?

Time：2021-7-19

Mean value and expectation are two concepts that we often contact with. As we all know, mean value is the number of values that are summed first and then divided; What’s the expectation. In order to make it easier to understand, ordinary people will say that you can also understand expectations as mean. Can it […]

• ## Some usages of dataframe

Time：2021-4-18

Some usages of dataframe in pandas Pandas reading excel file pd.read_excelThe prerequisite is to install the xlrd library Conversion between dataframe, numpy and list Dataframe to numpy: dataframe object. Values Dataframe to list: dataframe object values.tolist () List to numpy: np.array (list object) List to dataframe: pd.DataFrame (list object) Numpy to list: numpy object. Tolist […]

• ## The basic idea of normalization

Time：2021-4-1

sketch The basic idea of normalization is actually quite intuitive: because the distribution of the active input value (that is, x = Wu + B, u is the input) of the deep neural network before the nonlinear transformation gradually shifts or changes with the deepening of the network or during the training process,The reason why […]

• ## Implementation of double mean algorithm and red packet grabbing by golang

Time：2021-3-19

I’m sure you’re not unfamiliar with red packets, but have you ever thought about how to achieve it?First, we need to be clear about the requirements and the constraints of the requirements. There are three main restrictions on red envelopesa. The total amount of the red envelope = the total amount of the red envelope, […]

• ## The types and differences of t test in Statistics

Time：2021-3-3

As we said earlier, t-test is a test method used to compare whether there is a significant difference between two means. This article introduces the types of t-test and the specific Python implementation code. T-test is to compare the difference between two means. The difference of different kinds of t-test is actually the difference of […]

• ## On Chebyshev theorem in Statistics

Time：2021-2-27

We talked about the theorem of large numbers and the central limit theorem. Some readers left a message for us to talk about Chebyshev theorem. This article is about Chebyshev theorem. Before we talk about Chebyshev theorem, let’s look at Chebyshev inequality Where p is the probability, X is the random variable, μ is the […]

• ## FPN correction of image processing

Time：2021-2-26

1 introduction of FPN noise FPN (fixed pattern noise) is short for fixed pattern noise. According to the formation mechanism of FPN noise, it is divided into row FPN and column FPN. Line FPN: in tdi-cmos image sensor based on analog domain accumulation. Due to the parasitic resistance and capacitance in the analog accumulator circuit, […]

• ## Python data analysis: common data preprocessing methods

Time：2021-2-24

The text and pictures of this article are from the Internet, only for learning and communication, and do not have any commercial use. If you have any questions, please contact us in time. The following article comes from the data theory, author: wpc7113   Introduction to Python data analysis https://www.bilibili.com/video/BV18f4y1i7q9/   1. Standardization: to remove […]

• ## Gradient centralization: one line of code accelerates training and improves generalization ability | ECCV 2020 oral

Time：2021-2-20

Gradient centered GC can make the training of the network more stable and improve the generalization ability of the network. The algorithm is simple and the theoretical analysis of this paper is very sufficient, which can well explain the principle of GC  Source: Xiaofei’s algorithm Engineering Notes official account Thesis: gradient centralization: a new optimization […]