baharmahmudlu's repositories
coursera-counting-organisation
To get credit for this assignment, perform the instructions below and upload your SQLite3 database here: (Must have a .sqlite suffix) Hint: The top organizational count is 536. You do not need to export or convert the database - simply upload the .sqlite file that your program creates. See the example code for the use of the connect() statement. Counting Organizations This application will read the mailbox data (mbox.txt) and count the number of email messages per organization (i.e. domain name of the email address) using a database with the following schema to maintain the counts. CREATE TABLE Counts (org TEXT, count INTEGER) When you have run the program on mbox.txt upload the resulting database file above for grading. If you run the program multiple times in testing or with dfferent files, make sure to empty out the data before each run. You can use this code as a starting point for your application: http://www.py4e.com/code3/emaildb.py. The data file for this application is the same as in previous assignments: http://www.py4e.com/code3/mbox.txt. Because the sample code is using an UPDATE statement and committing the results to the database as each record is read in the loop, it might take as long as a few minutes to process all the data. The commit insists on completely writing all the data to disk every time it is called. The program can be speeded up greatly by moving the commit operation outside of the loop. In any database program, there is a balance between the number of operations you execute between commits and the importance of not losing the results of operations that have not yet been committed.
FaceRecognition
This project is about creating an algorithm to detect the face and return the name.
git_osx_installer
Installer for OS X
tensorflow-101
TensorFlow 101: Introduction to Deep Learning for Python
caliban
Research workflows made easy, locally and in the Cloud.
democoffee_lalala
funnnnnnnnn
machine-learning-project-walkthrough
An implementation of a complete machine learning solution in Python on a real-world dataset. This project is meant to demonstrate how all the steps of a machine learning pipeline come together to solve a problem!
Face-Detection-in-Python-using-OpenCV
Face Detection with Python using OpenCV
face_classification
Real-time face detection and emotion/gender classification using fer2013/imdb datasets with a keras CNN model and openCV.
face_recognition
The world's simplest facial recognition api for Python and the command line
hub
A library for transfer learning by reusing parts of TensorFlow models.