Artificial neural networks | Classification algorithms | Regression variable selection | Computational statistics
Group method of data handling (GMDH) is a family of inductive algorithms for computer-based mathematical modeling of multi-parametric datasets that features fully automatic structural and parametric optimization of models. GMDH is used in such fields as data mining, knowledge discovery, prediction, complex systems modeling, optimization and pattern recognition. GMDH algorithms are characterized by inductive procedure that performs sorting-out of gradually complicated polynomial models and selecting the best solution by means of the external criterion. A GMDH model with multiple inputs and one output is a subset of components of the base function (1): where fi are elementary functions dependent on different sets of inputs, ai are coefficients and m is the number of the base function components. In order to find the best solution, GMDH algorithms consider various component subsets of the base function (1) called partial models. Coefficients of these models are estimated by the least squares method. GMDH algorithms gradually increase the number of partial model components and find a model structure with optimal complexity indicated by the minimum value of an external criterion. This process is called self-organization of models. As the first base function used in GMDH, was the gradually complicated KolmogorovโGabor polynomial (2): Usually more simple partial models with up to second degree functions are used. The inductive algorithms are also known as polynomial neural networks. Jรผrgen Schmidhuber cites GMDH as one of the first deep learning methods, remarking that it was used to train eight-layer neural nets as early as 1971. (Wikipedia).
Grouped Data (1 of 2: Using Data groups to determine the frequency of dispersed data)
More resources available at www.misterwootube.com
From playlist Data Analysis
Mean of Grouped Frequency Tables
"Calculate mean from grouped frequency tables."
From playlist Data Handling: Frequency Tables
Reshape, Subset, and Summarize Data | Introduction to dplyr Part 2
We cover some basic functions of dplyr including the mighty group_by and summarize combo that makes dividing up datasets a breeze, as well as arrange, select, and filter that help get the data in a cleaner and more organized format. Group-by aggregation is one of the most powerful, yet sim
From playlist Introduction to dplyr
When should I use a "groupby" in pandas?
The pandas "groupby" method allows you to split a DataFrame into groups, apply a function to each group independently, and then combine the results back together. This is called the "split-apply-combine" pattern, and is a powerful tool for analyzing data across different categories. In thi
From playlist Data analysis in Python with pandas
Find the Mean of Grouped Data | Statistics, Grouped Frequency Tables
How do we find the mean of grouped data? If we are given a grouped frequency table to represent data, that presents a challenge because we do not know the raw data values - we only know how many fall into certain groups. Thus, we are unable to calculate the mean the normal way. We'll go ov
From playlist Statistics
Data Visualization: Types of Data
Here I introduce different types of data and highlight common ways to visualize them. Bing Brunton's website: www.bingbrunton.com
From playlist Intro to Data Science
We will look at the fundamental concept of clustering, different types of clustering methods and the weaknesses. Clustering is an unsupervised learning technique that consists of grouping data points and creating partitions based on similarity. The ultimate goal is to find groups of simila
From playlist Data Science in Minutes
Research Methods 1: Sampling Techniques
In this video, I discuss several types of sampling: random sampling, stratified random sampling, cluster sampling, systematic sampling, and convenience sampling. The figures presented are adopted/adapted from: https://www.pngkey.com/detail/u2y3q8q8e6o0u2t4_population-and-sample-graphic-de
From playlist Research Methods
Unity - using the Job System with ECS
From playlist Unity game engine
Unity - ECS transforms and rendering
From playlist Unity game engine
"Netty - The async event-driven network application framework" by Norman Maurer
Netty - The async event-driven network application frameworkย The internet and network is getting more important with every single day. So it may not be surprising to see that almost every application needs to interact over at least one of them these days. But writing high-performing netwo
From playlist Software Development Lectures
Data science describes the activities related to collecting, storing and creating value from data. Creating value from data means using it to do useful things, like making better decisions. By analyzing data we can detect patterns in it and understand the process that generated it. This i
From playlist Data Science Dictionary
Ju Sun - Toward practical phase retrieval with deep learning - IPAM at UCLA
Recorded 14 October 2022. Ju Sun of the University of Minnesota, Twin Cities, presents "Toward practical phase retrieval with deep learning" at IPAM's Diffractive Imaging with Phase Retrieval Workshop. Abstract: Phase retrieval (PR) concerns the recovery of a signal (1D, 2D, or 3D) from it
From playlist 2022 Diffractive Imaging with Phase Retrieval - - Computational Microscopy
Getting Started with Portfolio Optimization in MATLAB R2013a
Get a Free Trial: https://goo.gl/C2Y9A5 Get Pricing Info: https://goo.gl/kDvGHt Ready to Buy: https://goo.gl/vsIeA5 Create and optimize portfolios of assets using the portfolio object in Financial Toolbox, together with Datafeed Toolbox. For more videos, visit http://www.mathworks.com/pr
From playlist Computational Finance
Angular Live - 4 | Angular Forms Tutorial For Beginners | Angular Training | Edureka
๐ฅEdureka Angular 8 Certification Training: https://www.edureka.co/angular-training This Edureka "Angular Forms" video will help you learn about the Reactive and Template-driven forms of Angular. ๐นAngular 8 Tutorial: https://www.youtube.com/watch?v=pTec1e8oyc8 ๐นAngular Blog List: https://
From playlist Edureka Live Classes 2020
Data Analyst Interview Questions and Answers | Data Analytics Interview Questions | Edureka
๐ฅ๐๐๐ฎ๐ซ๐๐ค๐ ๐๐๐ญ๐ ๐๐ง๐๐ฅ๐ฒ๐ฌ๐ญ ๐๐จ๐ฎ๐ซ๐ฌ๐ (๐๐ฌ๐ ๐๐จ๐๐ "๐๐๐๐๐๐๐๐๐") : https://www.edureka.co/masters-program/data-analyst-certification In this Edureka Data Analyst Interview questions video, you will learn what kind of data analytics questions you can expect in the interview, How to answer them and much
From playlist Data Analytics with R Tutorial Videos
Pyspark RDD Tutorial | What Is RDD In Pyspark? | Pyspark Tutorial For Beginners | Simplilearn
This video on "PySpark RDD"" will provide you with a detailed and comprehensive knowledge of RDD. RDDs are the most important component of PySpark. Pyspark RDD is one of the fundamental data structures for handling both structured and unstructured data. ๐ฅEnroll for Free Python Course & Ge
From playlist Python For Beginners ๐ฅ[2022 Updated]
This lesson introduces the different sample methods when conducting a poll or survey. Site: http://mathispower4u.com
From playlist Introduction to Statistics