### Tag：transcendental

Time：2022-8-3

Just like transformer’s famous thesis “XX is all you need” was named Dafa, recently I have seen a lot of papers “unified XX framework for XX”. After all, who doesn’t like to write a set of frameworks and then move where they need to be–Convex. In this chapter, let’s look at how to transform ner’s […]

• ## Deep learning and CV tutorial (13) | target detection (SSD, Yolo Series)

Time：2022-7-26

Author:Han [email protected] Tutorial address：http://www.showmeai.tech/tutorials/37 Address of this article：http://www.showmeai.tech/article-detail/272 Statement: All Rights Reserved. Please contact the platform and author for reprint and indicate the source CollectionShowMeAISee more highlights This series isStanford cs231nA full set of study notes for deep learning for computer vision, and the corresponding course videos can be found inheresee. See the end of […]

• ## R language uses comprehensive information criteria to compare stochastic volatility (SV) models to model stock price time series

Time：2022-7-25

Original link: http://tecdat.cn/?p=23882 abstract Stochastic volatility (SV) model is a series of models commonly used in stock price modeling. In all SV models, volatility is regarded as a random time series. However, there are still great differences between SV models from the perspective of basic principles and parameter layout. Therefore, selecting the most appropriate SV […]

• ## Analysis of jags Bayesian regression model of extended tecdat|r language doctoral students delay completing their graduation thesis

Time：2022-7-16

Original link:http://tecdat.cn/?p=23652 This article provides readers with a basic tutorial on how to conduct Bayesian regression. It includes importing data files, exploring summary statistics and regression analysis. In this article, we first use the default a priori settings of the software. In the second step, we will apply the user specified priors and use Bayes […]

• ## PRML probability distribution

Time：2022-7-12

Address of this article:https://www.cnblogs.com/faranten/p/15917369.htmlPlease indicate the author and source for reprint 1 binary variable 1.1 Bernoulli distribution and binomial distribution ​ Consider one of the most basic experiments: the coin toss experiment. In an experiment, there are only two results, positive and negative, using random variables\(x=1\)To show that tossing a coin gets a positive,\(x=0\)To indicate […]

• ## Linear model of PRML regression

Time：2022-7-8

Address of this article:https://www.cnblogs.com/faranten/p/15947139.htmlPlease indicate the author and source for reprint​ ​ ​ The simplest form of a linear model is the linear model of input variables. However, by linearly combining a group of nonlinear functions of input variables, we can get a more useful class of functions. The focus of our discussion in this […]

• ## R language uses comprehensive information criterion to compare stochastic volatility (SV) model to model stock price time series

Time：2022-5-17

Original link: http://tecdat.cn/?p=23882  abstract Stochastic volatility (SV) model is a series of models commonly used in stock price modeling. In all SV models, volatility is regarded as a random time series. However, from the perspective of basic principle and parameter layout, there are still great differences between SV models. Therefore, choosing the most appropriate SV […]

• ## [model reasoning] quantization implementation share 3: explain the implementation of aciq symmetric quantization algorithm in detail

Time：2022-5-4

Welcome to follow my official account [Jizhi horizon] and reply to 001 to obtain Google programming specification   O_o   >_<   o_O   O_o   ~_~   o_O Hello, I’m Jizhi horizon. This paper analyzes the implementation of aciq symmetric quantization algorithm, taking Tengine’s implementation as an example. This is the third part of […]

• ## In depth understanding of tabnet: detailed architecture and classification code implementation

Time：2022-3-19

Tabnet released by Google is a neural network for tabular data. It realizes the feature selection of instance wise through the sequential attention mechanism similar to the additive model, and also realizes self supervised learning through the encoder decoder framework. Table data is the most commonly used data type in daily life. For example, credit […]

• ## 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 […]

• ## 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 […]

• ## R language Stan Bayesian linear regression model to analyze the impact of climate change on the northern hemisphere sea ice range and visually check the convergence of the model

Time：2022-2-8

Original link:http://tecdat.cn/?p=24334 1. UnderstandStan Like any statistical modeling, Bayesian modeling may need to design an appropriate model for your research problem, and then develop the model to make it conform to your data assumptions and run. Statistical models can be fitted in various packages of R or other statistical languages. But sometimes the perfect model […]