Deep Reinforcement learning has been a rising field in the last few years. A good approach to start with is the value-based method, where the state (or state-action) values are learned. In this post, a comprehensive review is provided where we focus on Q-learning and its extensions. — A Short Introduction to Reinforcement Learning (RL) There are three types of common machine learning approaches: 1) supervised learning, where a learning system learns a latent map based on labeled examples, 2) unsupervised learning, where a learning system establishes a model for data distribution based on unlabeled examples, and 3) Reinforcement Learning, where a decision-making system is…

The random forest model is considered one of the promising ML ensemble models that recently became highly popular. In this post, we review the last trends of the random forest. — Ensemble Models-Intro An ensemble considers multiple learning models and combines them to obtain a more powerful model. Combining different models into an ensemble leads to a better generalization of the data, minimizing the chance for overfitting. A random forest is an example of an ensemble model, where multiple decision trees are considered…

The Kalman filter is one of the most influential ideas used in Engineering, Economics, and Computer Science for real-time applications. This year we mention 60 years for the novel publication. This post is the first one in the series of “Kalman filter celebrates 60”. — I first came across the Kalman filter during my undergraduate studies when I took the navigation systems class. It was the last lecture, and the professor said it is out of the course syllabus, but if someone will deal with real-time applications, he is expected to meet it again. He…

A fundamental problem in geometry was solved using a Deep Neural Network (DNN). We learned a geometric property from examples in the supervised learning approach. As the simplest geometric object is a curve, we focused on learning the length of planar curves. For this reason, the fundamental length axioms were reconstructed and the ArcLengthNet was established. — Introduction The calculation of curve length is one of the most major components in many modern and classical problems. For example, a handwritten signature involves the computation of the length along the curve (Ooi et al.). When one handles the challenge of length computation in real-life problems he faces several constraints…

Navigating the Future: How AI and Inertial Sensors Are Transforming Surface Recognition in Vehicles — Introduction The collaboration between artificial intelligence (AI) and inertial sensors has brought about a new frontier in car surface recognition. This marriage of technology is enhancing safety, comfort, performance, and efficiency in unprecedented ways. Let’s explore how: 1. Redefining Comfort: Adaptive Suspension Systems

In this post, we deal with determining the appropriate ratio for training, validation, and test sets in small and large databases — Background Splitting a dataset into training, validation, and test sets is a crucial step in building a machine learning model, as it allows for the model to be trained on one set, tuned on another, and evaluated on a final set. Larger datasets benefit from more portion of training data while…

The Graph Convolutional Network (GCN) has revolutionized the field of deep learning by showcasing its versatility in solving real-world problems, including traffic prediction, which is a critical issue in transportation. — Introduction The Graph Convolutional Network (GCN) is a revolutionary development in the field of deep learning, demonstrating its versatility and potential for application in addressing real-world problems. One such challenge is traffic prediction, which is a critical issue in transportation. The ability to adapt GCN algorithms for traffic prediction purposes holds…

Attending a startup competition has several benefits for entrepreneurs and aspiring business owners, especially for AI startups. — For entrepreneurs and aspiring business owners seeking to take their ventures to the next level, participating in a startup competition is a prime opportunity for growth and development. …

Clustering is an unsupervised machine learning technique that involves dividing a set of unlabeled samples into groups, or clusters, based on their similarity. — Introduction Clustering is a way to group together data points that are similar to each other. Clustering can be used for exploring data, finding anomalies, and extracting features. It can be challenging to know how many groups to create. There are two main ways to group data: hard clustering and soft…