Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions or values from labeled historical data, enabling precise signals such as ...

Abstract: This paper aims at comparing the serial, shared memory parallelization, and distributed memory parallelization of the dynamic programming algorithm for the Knapsack Problem. Knapsack Problem ...

Abstract: This study addresses the optimal control problem for discrete-time nonlinear systems with fixed initial state. A fast policy iteration $(\text{PI})$ algorithm is developed to compute the ...

In this video, I explain how computer scientists simulate evolution to train or evolve AI. Explore the fascinating intersection of natural evolution and artificial intelligence. Young voters are ...

An artist’s impression of a quantum electrodynamics simulation using 100 qubits of an IBM quantum computer. The spheres and lines denote the qubits and connectivity of the IBM quantum processor; gold ...

Learn the Adagrad optimization algorithm, how it works, and how to implement it from scratch in Python for machine learning models. #Adagrad #Optimization #Python Trump administration looking to sell ...

A Matlab implementation of Dynamic Programming Algorithm for stereo matching. It provides vertical smoothness by trying to keep the current path close to the former path using an additional ...

Financial crime risk is not static. A customer’s risk profile can shift rapidly with new transactions, behaviors, or data. Yet historically, many financial institutions relied on one-time or ...