Artificial intelligence stands as a pivotal domain of science, characterized by continual innovation. MatDeck presently features an array of artificial intelligence algorithms within two expansive domains. One of these domains revolves around time series processing, with a strong emphasis on predicting future values for economic time series. The second domain centres on classification and machine learning.
MD Artificial Intelligence Functions
In the realm of time series predictions, particularly for economic data, the traditional approaches often involve methods like Box-Jenkins or ARIMA. The ARIMA model follows a three-stage iterative approach encompassing model identification and selection, parameter estimation, and model realization. In MatDeck, model identification employs autocorrelation and partial autocorrelation functions like pacf(). Parameter estimation relies on computation algorithms for maximum likelihood estimation, with Yule-Walker estimation (yulewalker()) serving to estimate AR parameters. Forecasting is accomplished using suitable models with the derived parameters via arforecasting(). To assess predictive capabilities, MatDeck offers various statistical error functions such as mean square error (mse()) and mean percentage error (mpe()).
AI Navies Bayes
MatDeck implements the Naive Bayes algorithm, an early and effective classification and machine learning technique. This approach involves making informed classification decisions by considering attribute probabilities for each class. It simplifies probability calculations by assuming attribute independence within each class.
MatDeck’s Naive Bayes implementation comprises two functions: naivebayest() for training and data preparation, and naivebayesc() for calculating posterior probabilities based on training data, facilitating informed classification decisions for attribute-defined records.
Regressions with AI
A singular line of code suffices for executing a linear regression and generating predictions using the regression model. The versatility extends to the usage of vectors for predicting values across extensive new datasets. Contrastingly, in the subsequent depiction, SciKit Learn—a prominent Python library for regressions—is employed.
Evidently, using SciKit Learn necessitates nearly seven times the amount of code to execute an equivalent regression and generate predictions for new data. This discrepancy isn’t merely limited to code quantity; it entails reshaping data and employing perplexing non-Pythonic functions to accomplish what can be achieved in a single line using MD Python. Ultimately, both approaches yield identical results, yet MD Python provides a more precise solution.
Creating PyTorch AI Models with No-Code
Our PyTorch AI generator presents a no-code resolution, facilitating the seamless generation and deployment of personalized AI models. Proficiency in coding or data science is not a prerequisite for its effective utilization. Our interface, designed for user-friendliness, simplifies the entire procedure, rendering it uncomplicated and instinctive. Employing a succinct and straightforward graphical user interface (GUI), you can swiftly produce exceptionally accurate PyTorch AI models in mere minutes. It comes available in Visionary Deck as well as all MatDeck versions.
We offer the flexibility to select from 1, 3, or 5-layer AI models and subsequently refine each layer precisely by adjusting the number of neurons and selecting the activation type. All of this can be accomplished using a dropdown menu, demanding minimal expertise or experience to craft an AI model of professional calibre.
Creating Google TensorFlow AI Models with No-Code
Our TensorFlow AI Generator is designed for those without coding expertise and enables the effortless creation and deployment of personalized AI models. Regardless of your background as a programmer or data scientist, our intuitive interface eliminates the need for complex coding. Create precise TensorFlow AI models swiftly using our user-friendly and succinct graphical user interface, all within a matter of minutes. The TensorFlow AI generator is also available in the ultra-affordable Visionary Deck as well as all other MatDeck versions.
Our no-code methodology ensures an immediate start, eliminating the need to invest months in mastering intricate programming languages or comprehending data science principles. This efficiency conserves both your time and resources, enabling you to concentrate on the pivotal objective: crafting an AI model of utmost precision and dependability.
You have the freedom to opt for AI Models with 1, 3, or 5 layers, each of which can be meticulously refined. This involves adjusting the number of neurons within each layer and selecting the appropriate activation type. These adjustments are effortlessly executed through a dropdown menu, demanding minimal to no expertise, and resulting in the creation of AI Models that exude professionalism.
Data Science and Handling
MD Products offer dedicated functions and toolboxes to import and export data with CSV, Excel and database files. Users can directly utilize read and write functions for these file formats in a MD Document or script to easily store all necessary data without any hiccups. Below we can see how easy it is to import and visualize data from an excel file.
You can also use our Excel Import and Export Toolboxes to access Excel data and write it directly to whichever variables you want; here we can see what the Excel Import Toolbox looks like when opened.
