Discover @arashalaei Open Source projects

Arash Alaei (@arashalaei)

arashalaei

Stars
31
Global Rank 479,903 (Top 17 %)
Followers 21
Following 30
Registered about 6 years ago
Most used languages

JavaScript
53.3 %

Python
16.7 %

Jupyter Notebook
13.3 %

CSS
3.3 %

Java
3.3 %

HTML
3.3 %

TypeScript
3.3 %

C
3.3 %
Location 🇮🇷 Iran
Country Total Rank 1,231
Country Ranking

JavaScript
266

Jupyter Notebook
294

C
367

TypeScript
578

CSS
726

Python
773

Java
806

HTML
906

pass-the-butter

In this project we want to implement some search algorithms like: IDS, Bidirectional BFS and A*

AI_Project_3

Decision_Tree_Regression_JS

mario

K-NN_JS

Support-Vector-Machine_JS

Multiple_Linear_Regression_JS

DS-in-TS

Natours-Backend

LU-Factorization

NLP_filter_toxic_comment

Toxic comments are detected and filtered using the naive Bayes classification

Genetic-algorithm

Implementation of different stages of genetic algorithm

react-project

os-final-project

Polynomial_Linear_Regression_JS

Simple_Linear_Regression_JS

Pig-Game

In this game, User Interface (UI) contains user/player that can do three things, they are as follows: Roll the dice, Hold and Reset

online-clothing-shop

Logistic_Regression_JS

Naive_Bayes_JS

Guess-My-Number

This simple project shows how to manipulate the DOM.

js-screenshot_node-version

Modal-Window

This mini project shows how to manipulate css classes:)

Random_Forest_Regression_JS

Natours-Design

monsters-rolodex

Familiarity with React basic concepts such as: class component, function component, state, props , ...

Cat_vs_No-Cat

The goal is to train a classifier that the input is an image represented by a feature vector, x, and predicts whether the corresponding label y is 1 or 0. In this case, whether this is a cat image (1) or a non-cat image (0).

Jupyter Notebook

Neural-networks-optimization-methods

Until now, I've always used Gradient Descent to update the parameters and minimize the cost. In this notebook, i will learn more advanced optimization methods that can speed up learning and perhaps even get me to a better final value for the cost function. Having a good optimization algorithm can be the difference between waiting days vs. just a few hours to get a good result.

Jupyter Notebook

2-layer-neural-network

It's time to build my first neural network, which will have a hidden layer. You will see a big difference between this model and the one i implemented using logistic regression(cat vs not-cat)

Jupyter Notebook

Deep_neural_network

I have previously trained a 2-layer Neural Network (with a single hidden layer). In this project, i will build a deep neural network, with as many layers as i want!

Jupyter Notebook