popular machine learning algorithms

the correct algorithm, which involves the so-called Stein identity and kernelized Stein discrenpancy. This is no longer the case, interested readers can pay attention to the original text. The experimental part of the article is relatively simple, first to a one-dimensional Gaussian distribution situation did validation, to ensure that can run. The experiment was followed in the Bayesian Logistic regression and the Bayesian neural network, which contrasted a series of methods and datasets. Over

1. Integrated Learning Overview1.1 Integrated Learning OverviewIntegration learning has a higher quasi-rate in machine learning algorithms, the disadvantage is that the training process of the model may be more complicated and the

There is a period of time does not dry goods, home are to be the weekly lyrics occupied, do not write anything to become salted fish. Get to the point. The goal of this tutorial is obvious: practice. Further, when you learn some knowledge about machine learning, how to deepen the understanding of the content through practice. Here, we make an example from the 2nd-part perceptron of Dr. Hangyuan Li's statist

Python machine learning decision tree and python machine Decision Tree Decision tree (DTs) is an unsupervised learning method for classification and regression. Advantages: low computing complexity, easy to understand output results, insensitive to missing median values, and the ability to process irrelevant feature da

In the previous sections, we have covered what is target detection and how to detect targets, as well as the concepts of sliding windows, bounding box, and IOU, non-maxima suppression.Here will summarize the current target detection research results, and several classical target detection algorithms to summarize, this article is based on deep learning target detection, in the following sections, will be spe

superset of that element are infrequent. The Apriori algorithm starts with a single-element itemsets and forms a larger set by combining itemsets that meet the minimum support requirements. The degree of support is used to measure how often a collection appears in the original data.2.10 Fp-growth algorithm:Description: Fp-growth is also an algorithm for discovering frequent itemsets, and he uses the structure of the FP tree to store building elements, and other apriori

relationship scenarios. In recent years, the most popular neural network algorithm, which can deal with many problems in the field of machine learning. Neural network algorithms have the ability of linear and nonlinear learning algorithms.Neural networks are inspired by bio

Unsupervised Learning2.2.1 Data Clustering2.2.1.1 K mean value algorithm (K-means)2.2.2 Features reduced dimension2.2.2.1 principal component Analysis (Principal Component ANALYSIS:PCA)3.1 Model Usage Tips3.1.1 Feature Enhancement3.1.1.1 Feature Extraction3.1.1.2 Feature ScreeningRegularization of the 3.1.2 model3.1.2.1 Under-fitting and over-fitting3.1.2.2 L1 Norm regularization3.1.2.3 L2 Norm regularization3.1.3 Model Test3.1.3.1 Leave a verification3.1.3.2 Cross-validation3.1.4 Super Pa

production as early as possible. Although early stages of investment often involve building and debugging tools, this makes creating and debugging product services simple and interesting. Keep it simple.Do not spend too much effort on complex code libraries or frameworks.When there is a simpler solution that is good enough to use simple things, don't think about it any more. For example, you can use a simple HTTP Communication Protocol instead of a popul