Features selector based on the self selected-algorithm, loss function and validation method
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Updated
May 8, 2019 - Python
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.
Visualization for multiple searching algorithms.
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
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
A PyTorch implementation of Transformers from scratch for Machine Translation based on "Attention Is All You Need" by Ashish Vaswani et. al.
A project for Fundamental of Optimization class at HUST, Winter 2022
Academic Assignment on Search Algorithms Presented in the Fundamentals of Intelligent Systems Course (2023/1).
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
graph search algorithms depicted on a nxn matrix graph
8 puzzle solver, a python program that solves the modified version of Expense 8 puzzle problem using different algorithms,
Here are my solved computer tasks for the Artificial Intelligence subject
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