
JVL
One Nice Bug Per Day

oozey mess

titsay
Monterey Bay Aquarium

izzy's playlists!

Product Placement
Today's Document
PUT YOUR BEARD IN MY MOUTH
taylor price
No title available

❣ Chile in a Photography ❣
wallacepolsom
dirt enthusiast
AnasAbdin
Acquired Stardust
YOU ARE THE REASON
Keni
Not today Justin
art blog(derogatory)
seen from Belgium
seen from United States

seen from United States

seen from United States

seen from Malaysia

seen from Türkiye
seen from United States
seen from United States

seen from United Arab Emirates
seen from United States
seen from United States

seen from United States
seen from United States
seen from United States
seen from Canada
seen from United States

seen from United States
seen from United States
seen from Sri Lanka

seen from Jordan
@elydenis
The Smith Chart. The Smith chart, also known as a polar impedance plot, was invented by Philip Smith in 1939 to plot the characteristics of microwave components such as reflection coefficient, impedance, and admittance. With the help of a Smith chart, complex mathematical equations can be simplified. The Smith Chart is the graphical representation of a complex mathematical equation. It is the circular plot of the characteristics of microwave components. It is the most used tool for microwave engineers to visualize complex-valued quantities and calculate the mapping between them. It consists of two sets of circles for plotting various parameters of mismatched transmission lines. One is the set of complete circles whose centers lie on the straight line and the other one is the set of two arc circles which lie on the either sides of the straight line. The horizontal axis represents the normalized resistance and the normalized line reactance is shown on the outer edge of the circles. The complete circle of the Smith Chart represents a half wavelength along the straight line.
Source: Flowing Data
Machine Learning and Data Mining Algorithms
Decision Trees: C4.5. Note that there are many different tree-based algorithms. Try to use C4.5 that has some advantages over the classical CART algorithm. For example, CART works only with binary tests (YES/NO) but C4.5 can work with more than two outcomes.
Decision Trees: CART. This tree-based algorithm partitions cases in a binary way and can deal with both numerical and categorical values. The method has its applications in medical research, biology, electrical engineering, marketing and other fields.
The K-means algorithm has been created to divide a given set of cases into k clusters. The algorithm starts by picking initial representatives of clusters and then iteratively redistributes data to clusters. The algorithm performs a series of iterations until convergence.
Support Vector Machines (SVMs). At present, this class of algorithms provides users with robust calculations for solving classification problems. It does not require a lot of training cases and it works well with multidimensional tasks, which makes it very useful for working with Big Data.
The Expectation-Minimization (EM) algorithm. It finds maximum likelihood in statistical models parameters in cases where the model parameters cannot be observed. EM is used in a variety of fields, for example, for the reconstruction of medical images.
AdaBoost. It relates to ensemble learning methods that use a set of algorithms to achieve better performance than with the help of a single algorithm. This algorithm is very simple and has very precise predictions. It is applied for solving many kinds of classification problems, in particular, for face detection.
Naïve Bayes. The main advantage of this old method is that this algorithm is very simple and it does not require complicated iterative procedures. This makes it efficient for working with big data.
The Apriori algorithm has been designed for working with associations in transaction datasets. It discovers frequent sets of items (for example, frequent sets of purchases in a supermarket) and then finds out association rules based on these itemsets. For example, “if a customer visits a certain webpage, he/she is likely to visit the conversion page”.
Genetic Algorithms. These heuristics imitate natural selection processes with mutation and crossover that can be observed in nature. They are especially useful for finding complicated non-linear dependencies in data.
The PageRank algorithm. It calculates ranks of websites in search results based on using hyperlinks. The main idea of this algorithm is that it measures numerically the significance of webpages.
Source: onthe.io
dimple is interesting for several reasons. It’s built on d3, gets legends, mouseovers, and axes right, but most importantly, it has an elegant multi-dimensional approach where “measure” and “category” axes can be added and mixed together however you like.
Where other libraries might treat “stacked” bars as a completely different concept than “grouped” bars, or implement a pie chart separately from a bubble chart, dimple allows you to mix and match. This makes it a far more expressive library for exploratory data visualization in high dimensional spaces.
Proof in point - every visualization in the image is drawn from the same data file loaded from the server. By mixing and matching different columns from the data set you can quickly find the most interesting views.
Source: The Asimov Institute
Kohonen, Teuvo. “Self-organized formation of topologically correct feature maps.” Biological cybernetics 43.1 (1982): 59-69.
Introduction to Bayesian Inference
Bayesian inference is an extremely powerful set of tools for modeling any random variable, such as the value of a regression parameter, a demographic statistic, a business KPI, or the part of speech of a word. We provide our understanding of a problem and some data, and in return get a quantitative measure of how certain we are of a particular fact.
Source: Data Science
Introduction to K-means Clustering
K-means clustering is a type of unsupervised learning, which is used when you have unlabeled data (i.e., data without defined categories or groups). The goal of this algorithm is to find groups in the data, with the number of groups represented by the variable K. The algorithm works iteratively to assign each data point to one of K groups based on the features that are provided. Data points are clustered based on feature similarity. The results of the K-means clustering algorithm are:
1. The centroids of the K clusters, which can be used to label new data
2. Labels for the training data (each data point is assigned to a single cluster)
Rather than defining groups before looking at the data, clustering allows you to find and analyze the groups that have formed organically. The "Choosing K" section below describes how the number of groups can be determined.
Source: Data Science
Source: DHL
Source: Martin Grandjean
Source: Circo
An Introduction to Clustering
Table of Contents
Overview
Types of Clustering
Types of Clustering Algorithms
K Means Clustering
Hierarchical Clustering
Difference between K Means and Hierarchical clustering
Applications of Clustering
Improving Supervised Learning algorithms with clustering
Source: Analytics Vidhya
Free Exploratory Data Analysis Tools
Excel / Spreadsheet
Trifacta
Rapid Miner
Rattle GUI
Qlikview
Weka
KNIME
Orange
Tableau Public
Data Wrapper
Data Science Studio (DSS)
OpenRefine
Talend
Data Preparator
DataCracker
Data Applied
Tanagra Project
H2o
Source: Analytics Vidhya
Mind Mapping
List of 12 Mind Mapping Tools
XMind
Coggle
Freemind
Wisemapping
Mind42
LucidChart
MindManager
SpiderScribe
Bubbl.us
Freeplane
MindApp
Text2MindMap
Source: Analytics Vidhya
Internet of Things (IoT)
List of Books on Internet of Things
The Second Machine Age: Work, Progress and Prosperity in a Time of Brilliant Technologies
Getting started with Internet of Things
The Silent Intelligence
IoT Disruptions: The Internet of Things – Innovation & Jobs
Meta Products: Building the Internet of Things
Everyware: The dawning age of ubiquitous computing
Trillions
Designing Connected Products
Learning Internet of Things
Big Data and The Internet of Things
The Design of Everyday Things
Source: Analytics Vidhya