A repository of multiple projects and labs done in the Udacity Artificial Intelligence Nanodegree (aind).
In this project, we will be writing code to implement two extensions of our sudoku solver. The first one will be to implement the technique called "naked twins". The second one will be to modify our existing code to solve a diagonal sudoku. To complete this project we will use the tools we learned about in the lesson, and build upon them.
In this lesson, we'll build a Game-Playing agent that defeats opponents in Isolation. Along the way, we'll learn about advanced Game-Playing techniques such as Minimax, Iterative Deepening, and Alpha-Beta Pruning.
In this lab, we'll use depth-first search, breadth-first search uniform-cost search and A* search to build a pacman game playing agent who can find the shortest way through the maze to the goal and reach there.
In this exercise we will check our understanding of simulated annealing by implementing the algorithm in a Jupyter notebook and using it to solve the Traveling Salesman Problem (TSP) between US state capitals.
In this exercise we will check our understanding of Constraint Satisfaction Problems by solving the N-queens problem using symbolic constraints and backtracking search in a Jupyter notebook.
In this project, we will build a system that can recognize words communicated using the American Sign Language (ASL). We will be provided a preprocessed dataset of tracked hand and nose positions extracted from video. Our goal would be to train a set of Hidden Markov Models (HMMs) using part of this dataset to try and identify individual words from test sequences.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent