Top 8 open source AI technologies in machine learning Artificial intelligence (AI) technologies are quickly transforming almost every sphere of our lives. From how we communicate to the means we use for transportation, we seem to be getting increasingly addicted to them. Because of these rapid advancements, massive amounts of talent and resources are dedicated to accelerating the growth of the technologies. Here is a list of 8 best open source AI technologies you can use to take your machine learning projects to the next level. 1. TensorFlow
Crossfilter Fast Multidimensional Filtering for Coordinated Views Crossfilter is a JavaScript library for exploring large multivariate datasets in the browser. Crossfilter supports extremely fast (<30ms) interaction with coordinated views, even with datasets containing a million or more records; we built it to power analytics for Square Register, allowing merchants to slice and dice their payment history fluidly. Since most interactions only involve a single dimension, and then only small adjustments are made to the filter values, incremental filtering and reducing is significantly faster than starting from scratch. Crossfilter uses sorted indexes (and a few bit-twiddling hacks) to make this possible, dramatically increasing the perforÂmance of live histograms and top-K lists.
COC131 Data Mining, Tuotorials Weka "The overall goal of our project is to build a state-of-the-art facility for developing machine learning (ML) techniques and to apply them to real-world data mining problems. Our team has incorporated several standard ML techniques into a software "workbench" called WEKA, for Waikato Environment for Knowledge Analysis. With it, a specialist in a particular field is able to use ML to derive useful knowledge from databases that are far too large to be analysed by hand.
What is R? During the last decade, the momentum coming from both academia and industry has lifted the R programming language to become the single most important tool for computational statistics, visualization and data science. Worldwide, millions of statisticians and data scientists use R to solve their most challenging problems in fields ranging from computational biology to quantitative marketing. R has become the most popular language for data science and an essential tool for Finance and analytics-driven companies such as Google, Facebook, and LinkedIn.
FAQ: Using a plugin to connect to a database How do I connect to a database by using a Stata plugin? ODBC vs. plugin The easiest way to import data from a database directly into Stata is to use the odbc command. However, there are occasions where the odbc command will not work or is not the best solution for importing data. For example, the odbc command will not work on your operating system (Solaris), there is not an ODBC driver for the database in question, or ODBC is too slow. Native American Tea Cures Cancer. Kept Secret for Over 100 years! by PAUL FASSA A simple inexpensive four herb tea that cures cancer? Even AIDS maybe?
Octave GNU Octave is a high-level interpreted language, primarily intended for numerical computations. It provides capabilities for the numerical solution of linear and nonlinear problems, and for performing other numerical experiments. It also provides extensive graphics capabilities for data visualization and manipulation. Octave is normally used through its interactive command line interface, but it can also be used to write non-interactive programs.
Julia Studio Beginner These tutorials will help you to familiarize yourself with the Julia Studio environment and the basics of the language. Hello, World! 1 Star This tutorial will walk you through creating a project in Julia Studio and have your write your first program. 8.7 ARIMA modelling in R How does auto.arima() work ? The auto.arima() function in R uses a variation of the Hyndman and Khandakar algorithm which combines unit root tests, minimization of the AICc and MLE to obtain an ARIMA model. The algorithm follows these steps. Hyndman-Khandakar algorithm for automatic ARIMA modelling The number of differences $d$ is determined using repeated KPSS tests.The values of $p$ and $q$ are then chosen by minimizing the AICc after differencing the data $d$ times.
Why are some mushrooms 'magic?' Study offers evolutionary explanation Psychedelic mushrooms likely developed their "magical" properties to trip up fungi-munching insects, suggests new research. The work helps explain a biological mystery and could open scientific doors to studies of novel treatments for neurological disease, said lead researcher Jason Slot, an assistant professor of fungal evolutionary genomics at The Ohio State University. Mushrooms that contain the brain-altering compound psilocybin vary widely in terms of their biological lineage and, on the surface, don't appear to have a whole lot in common, he said. From an evolutionary biology perspective, that is intriguing and points to a phenomenon in which genetic material hops from one species to another - a process called horizontal gene transfer, Slot said. When it happens in nature, it's typically in response to stressors or opportunities in the environment. "But our main question is, 'How did it evolve?'"
Weka 3 - Data Mining with Open Source Machine Learning Software in Java Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes. Found only on the islands of New Zealand, the Weka is a flightless bird with an inquisitive nature. The name is pronounced like this, and the bird sounds like this.
Federal Reserve Economic Data Skip to main content Economic Research Federal Reserve Bank of St. Louis My Account Register Sign in Download, graph, and track 385,000 US and international time series from 80 sources. ARIMA Modelling of Time Series Description Fit an ARIMA model to a univariate time series. Usage arima(x, order = c(0L, 0L, 0L), seasonal = list(order = c(0L, 0L, 0L), period = NA), xreg = NULL, include.mean = TRUE, transform.pars = TRUE, fixed = NULL, init = NULL, method = c("CSS-ML", "ML", "CSS"), n.cond, SSinit = c("Gardner1980", "Rossignol2011"), optim.method = "BFGS", optim.control = list(), kappa = 1e6)