• ## Asicboost and segwit

Time：2022-3-21

The discussion on asicboost and segwit has been deserted a lot, but I still want to try to explain it from a technical point of view: What is asicboost What is the relationship between asicboost and segwit Before saying these two things, we cannot do without a keyword:mining, let’s talk about mining first! mining Mining […]

• ## Data update or event trigger in Vue, but the view is not updated

Time：2022-3-18

Problem source In the development process, it is necessary to fix the tooltip of echarts trend chart, The implementation scheme is to set the always showcontent of the tooltip to true and the trigger on to none when clicking item, so that the tooltip will not be triggered, After clicking the external area of the […]

• ## Troubleshooting ideas of feignclient annotation attribute configuration not taking effect

Time：2022-3-18

Troubleshooting ideas of feignclient annotation attribute configuration not taking effect Problem background We know that “if you need to customize a single feign configuration, the class of the @ configuration annotation of feign cannot overlap with the package of @ componentscan. In this way, if the package overlaps, all feign clients will use the configuration”. […]

• ## Machine learning algorithm series (VIII) – logarithmic probability regression algorithm (II) (logistic regression algorithm)

Time：2022-3-15

Background knowledge required for reading this article: logarithmic probability regression algorithm (I), conjugate gradient method, and a little programming knowledge 1、 Introduction The last article is the logarithmic probability regression algorithm (I), which introduces two methods to optimize the cost function of logarithmic probability regression – gradient descent method and Newton’s method. However, when using […]

• ## Python Bayesian inference calculation: inferring probability and visual cases with beta prior distribution

Time：2022-3-12

Original link: http://tecdat.cn/?p=24084 In this article, I will extend the example of inferring probabilities from data to consider all (continuous) values between 0 and 1, rather than a discrete set of candidate probabilities. This means that our a priori (and a posteriori) is now a probability density function (PDF) rather than a probability mass function […]

• ## Exploration and utilization of reinforcement learning

Time：2022-3-11

1、 Brief description In the process of continuous interaction with the environment, the agent keeps exploring in different states and obtains the feedback of different actions. Exploration can help agents obtain feedback through continuous experiments. Exploration refers to using the existing feedback information to select the best action. Therefore, how to balance exploration and utilization […]

• ## Machine learning algorithm series (IX) – multiple logistic regression

Time：2022-3-4

Background knowledge required for reading this article: logarithmic probability regression algorithm and yidui programming knowledge 1、 Introduction    the logarithmic probability regression algorithm introduced earlier is called regression algorithm, but it is actually used to deal with classification problems. The data set is divided into two categories, represented by 0, 1 or – 1, 1. […]

• ## Python Bayesian probability inference sequence data probability and a priori, likelihood and a posteriori graph visualization

Time：2022-2-10

Original link: http://tecdat.cn/?p=24191 In this article, I will focus on an example of the inference probability given a short data sequence. I will first introduce the theory of how to use Bayesian method for expectation reasoning, and then implement the theory in Python so that we can deal with these ideas. In order to make […]

• ## 100 thinking models – 40 Law of large numbers

Time：2022-2-8

In the large number of repeated occurrences of random events, it often presents an almost inevitable law, which is the law of large numbers. Generally speaking, this theorem is that under the condition of constant test, the frequency of random events is similar to its probability. There is a certain necessity in chance. From the […]

• ## Detailed interpretation and mapping of qqplot of 10x single cell (10x spatial transcriptome) Seurat analysis

Time：2022-2-6

The following picture should be familiar to everyone jsplots-1.png This graph is a functionJackStrawPlot()We should all know how many principal components (PCS) are used for downstream analysis. Let’s take a look at the explanation of this figure. Plots the results of the JackStraw analysis for PCA significance. For each PC, plots a QQ-plot comparing the […]

• ## JS calculates the winning probability according to the prize weight

Time：2022-2-1

catalogue 1、 Sample scenario 1.1. Set the award name of the lucky draw 1.2. Set the weight of each award 1.3 rules of lucky draw 2、 Implementation principle 2.1. Calculate weights and values 2.2. Write lottery function 3、 Project complete code 1、 Sample scenario 1.1. Set the award name of the lucky draw Name of […]

• ## Those things about Chinese ner 1 Detailed explanation of Bert bilstm CRF baseline model & code implementation

Time：2022-1-28

In this series, let’s talk about Chinese entity recognition in sequence annotation. In the first chapter, let’s start with the current common benchmark model Bert + bilstm + CRF to see what problems have been solved by this model and what problems remain to be solved. See the following model implementation and evaluation scripts for […]