Real-time global intelligence dashboard. AI-powered news aggregation, geopolitical monitoring, and infrastructure tracking in a unified situational awareness interface

In-depth tutorials on LLMs, RAGs and real-world AI agent applications.

Using KANs and comparing them to symbolic regression

R Package for working with radiometric thermal image files and data

Interactive web-based dashboard to manage traffic flow using YOLOX, DeepSORT

Voyager's Golden Disk Image Browser

Train and predict your model on pre-trained deep learning models through the GUI (web app). No more many parameters, no more data preprocessing.

Bayesian Modeling and Probabilistic Programming in Python

given run to failure measurements of various sensors on a sample of similar jet engines, estimate the remaining useful life (RUL) of a new jet engine that has measurements of the same sensor for a …

Data exploration and analysis of Saudi Arabia energy consumption pattern. I explore energy consumption patterns throughout the years and comment on the large growth of energy consumption.

A tutorial for creating a gif that displays the land surface temperature (LST) for a selected part of a city, alongside an optical image obtained within the same time frame

AiTLAS implements state-of-the-art AI methods for exploratory and predictive analysis of satellite images.

An Exploration into Convolutional Neural Networks

This methods can be used for event identification to detect first arrival time of Vp and Vs. These methods are Akaike Information Criterion, MER and S/L kurt

Predict remaining useful life of a component based on historical sensor observations using automated feature engineering

A game theoretic approach to explain the output of any machine learning model.

Code associated with our paper "Graph2Speak: Improving Speaker Identification with Network Knowledge in Criminal Conversational Data"

Python code source for features selection 👨‍🔬 series on medium website. 📰

Python code source for features selection 👨‍🔬 series on medium website. 📰

XAI Notebooks with LIME

Tutorial on how to use the SHAP library to explain the feature importance with Shapley values.

Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

Notebook demonstrating use of LIME to interpret a model of long-term relationship success

FastAI Model Interpretation with LIME

XAI Notebooks with LIME

Implementing LIME to explain Naïve Bayes, Random Forest, Logistic Regression, XGBoost, and a Feedforward Neural Network classifiers making binary predictions.

Best transfer learning and domain adaptation resources (papers, tutorials, datasets, etc.)

Implementation of many transfer learning algorithms in Python with Jupyter notebooks

Mean shift is a non-parametric feature-space analysis technique for locating the maxima of a density function, a so-called mode-seeking algorithm. Application domains include cluster analysis in co…