Discover M-Taghizadeh/Flask-Mail Open Source project

Full Python Programming Tutorials with Focus on artificial intelligence and machine learning

Python-full-course

In this implementation, using the Flan T5 large language model, we performed the Text Classification task on the IMDB dataset and obtained a very good accuracy of 93%.

flan-t5-base-imdb-text-classification

Implemetation Denial of services attack with python (socket programming and threading)

PyDOS

In this repository, based on the latest NLP pappers, we researched on sequential data and time series and developed tasks in NLP such as stock price prediction, time series prediction, sentiment analysis from text and We developed the language model and so on. This research is based on recurrent neural networks, LSTM networks and the new Transformer architecture and attention mechanism.

NLP-from-RNN-to-Transformer

Some of informations about Mohammad Taghizadeh

M-Taghizadeh

Git-tutorial-for-developers

Here we learn Git SC and this repository is for communication with students of Git full course

PyGram

PyGram is a social network that developed with python.

In this repository, we implement common image processing techniques in Python and fully describe their algorithms.

Image-Processing-Python

implementation of design patterns with python

Python-Design-Patterns

Implement a simple Restful API on Django using the following tech stack: Python, Django Rest Framework, AWS DynamoDB

Django-Restful-API-Challenge

Our goal in this webinar is to provide a quick and practical training as your first step to becoming a professional Python programmer, so that after watching this training you will be able to gain a very good knowledge of programming with Python and using Python in artificial intelligence, machine learning, deep learning, data mining, and backend programming using Flask and Django

python-webinar

In this repository, we implement and review state of the art papers in the field of face recognition and face detection, and perform operations such as face verification and face identification with Deep models like Arcface, MTCNN, Facenet and so on.

Deep-Face-Recognition

In this repository, we introduced ChatGPT applications for Python and artificial intelligence programmers in various cases.

ChatGPT-for-Python-Programmers

BigData_Projects

implementation of md5 algorithm using python pure

MD5_Algorithm

design compiler with c++ language in Qt platform

Compiler-Design-

CRUD in ReSTFul API in Flask

ReSTFul_API_Intro

Site development provides the latest statistics on the coronavirus

Corona-Project

PyChat is a massanger that implemented with python (socket programming)

PyChat

in this repo we learn about MongoDB and Python mongodb (PyMongoDB)

PyMongoDB

K Nearest Neighbor Classifier for 1 or multi features.

KNN_Classifier

Calculator Application with Java language (Android Application)

App_Calculator

Desktop Application for a University with java language.

University

sound reverser in matlab...

SOLID-Principle-Python

soundReverse

MATLAB

Puzzle-Game-

Puzzle game with java language.

Wumpus-World-Algorithm-Design-

wumpus world..

Steganography and DeSteganography for Audio into Video file...

Steganography

MATLAB

JavaGram-Data-Base-

Data Base Creation Documentaries for JavaGram (Social Network)

Dictionary-link-list-approach

Dictionary link list approach using c++ language.

Implement the des algorithm with python

PyDES

Travelling salesman problem with Genetic Algorithm.

Travelling-salesman-problem-Genetic-Algorithm

implementation of sha1 hash function algorithm with python pure

SHA1_algorithm

In This repository explains the new features in Python 3.10

py310

Magic-Square

Magic Square using c++

Sparse multiplication using c++

Sparse-multiplication

Dormitory-Project

JavaGram is a social network that was developed and design by java programming language.

sqlDocs

Sql Docs...

JavaGram_SocialNetwork

University with Linked List approach.

University-with-Linked-List-approach.

implementation of decision tree algorithm from scratch with python

Py_Decision-Tree

Pacman

Pacman Game using java

calculation of video's duration ...

cal_video_duration

Implementation Search Engine with LTC-LNC algorithm developed with Python and Flask framework

LtcLnc-Algorithm

Travelling salesman problem.

Travelling-salesman-problem-Dynamic-Programming

ReST-Directory

get image from webcam by runing this python script...

webcamGetPic

FlaskBlog

Transfer learning (TL) is a research problem in machine learning (ML) that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem.

VGG16

Bayes Classification for one feature

Bayes-Classifier1Feature

256 is a familiar number for programmers, and for this reason, the 256th day every year is called programmer's day. In this webinar, we celebrated together and learned some tips to improve our career path in programming.

Programming-Day-Webinar

Bayes_Classifier

In machine learning, naïve Bayes classifiers are a family of simple "probabilistic classifiers" based on applying Bayes' theorem with strong (naïve) independence assumptions between the features. They are among the simplest Bayesian network models.

Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components. This transformation is defined in such a way that the first principal component has the largest possible variance (that is, accounts for as much of the variability in the data as possible), and each succeeding component in turn has the highest variance possible under the constraint that it is orthogonal to the preceding components. The resulting vectors (each being a linear combination of the variables and containing n observations) are an uncorrelated orthogonal basis set. PCA is sensitive to the relative scaling of the original variables.

PCA

In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the 'outcome variable') and one or more independent variables (often called 'predictors', 'covariates', or 'features'). The most common form of regression analysis is linear regression, in which a researcher finds the line (or a more complex linear function) that most closely fits the data according to a specific mathematical criterion. For example, the method of ordinary least squares computes the unique line (or hyperplane) that minimizes the sum of squared distances between the true data and that line (or hyperplane).

Regression

Common-Ground-Experiments