Features selector based on the self selected-algorithm, loss function and validation method

PyTorch implementation for Seq2Seq model with attention and Greedy Search / Beam Search for neural machine translation

CSE 571 Artificial Intelligence

BFS, IDS, Greedy & A* applied to the 8-puzzle problem. ⚙️

This is an educational repository containing implementation of some search algorithms in Artificial Intelligence.

A fine-tuned visual implementation of Informed and Uninformed Search Algorithms such as Breadth First Search, Depth First Search, Uniform Cost Search, A* Search, Greedy First Search

Visualization for multiple searching algorithms.

N-Puzzle implementation with BFS, DFS, Greedy and A*

Sliding Puzzle solver and utilities

Repositorio sobre los algoritmos devoradores. Se presentará un esquema general, descripición, elementos que lo componen y ejemplos.

Original implementation of SA in knapsack problem, using Python

Academic Assignment on Search Algorithms Presented in the Fundamentals of Intelligent Systems Course (2023/1).

A project for Fundamental of Optimization class at HUST, Winter 2022

A PyTorch implementation of Transformers from scratch for Machine Translation based on "Attention Is All You Need" by Ashish Vaswani et. al.

Solving sudoku puzzles using a variation of search algorithms

graph search algorithms depicted on a nxn matrix graph

This is an implementation of the risk board game with various agents, (naive + intelligent). AI agents are Greedy, A*, A*-real-time

This repository contains implementation of different AI algorithms, based on the 4th edition of amazing AI Book, Artificial Intelligence A Modern Approach

This project, developed for an Introduction to Artificial Intelligence course, focuses on implementing search algorithms to solve pathfinding problems. It demonstrates foundational AI concepts such as uniform-cost search (UCS), greedy search, and A* search, with customizable data structures for optimal performance.