tankietse

tankietse's Stars

• EbookFoundation/free-programming-books

  :books: Freely available programming books

  337k61.6k

• esri-es/koop-provider-postgresql

  Expose an API to return PostgreSQL queries like ArcGIS Feature Layers

  Language:HTML53

• weatherlayers/cpt2js

  Color palette text parser to a function, compatible with GMT, GDAL, GRASS, PostGIS, ArcGIS

  Language:TypeScript8

• jakobzhao/geog328

  Web GIS

  Language:HTML18230

• sshuair/awesome-gis

  😎Awesome GIS is a collection of geospatial related sources, including cartographic tools, geoanalysis tools, developer tools, data, conference & communities, news, massive open online course, some amazing map sites, and more.

  4.5k730

• opengeos/qgis-basemaps

  Adding basemaps to QGIS

  Language:Python9816

• Esri/arcgis-maps-sdk-flutter-samples

  Sample code for ArcGIS Maps SDK for Flutter

  Language:Dart135

• Solexofficial/react-hotel_booking

  Отель. Бронирование номеров

  Language:TypeScript10924

• suno-ai/bark

  🔊 Text-Prompted Generative Audio Model

  Language:Jupyter Notebook35.9k4.2k

• timlrx/tailwind-nextjs-starter-blog

  This is a Next.js, Tailwind CSS blogging starter template. Comes out of the box configured with the latest technologies to make technical writing a breeze. Easily configurable and customizable. Perfect as a replacement to existing Jekyll and Hugo individual blogs.

  Language:TypeScript8.8k2k

• ahmedshahriar/HR-Management-System

  Language:PHP91

• delee03/CyberPhone_SASS

  Cybersoft Academy - BS12 | Sass

  Language:HTML1

• delee03/Ba-Anh-Em

  Project _Website đặt vé xem phim | HUTECH University

  21

• delee03/react_glasses

  Cybersoft Academy - BS12 | ReactJS - Thử kính với State

  Language:JavaScript1

• delee03/basic_react

  Cybersoft Academy - BS12 | ReactJS Learning

  Language:JavaScript1

• delee03/shoes_store

  Cybersoft Academy - BS12 | Capstone Project API ES6 Sass

  Language:HTML1

• huseinzol05/Stock-Prediction-Models

  Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations

  Language:Jupyter Notebook8k2.8k

• mshaikh2/AttentionHandwritingVerification

  ICFHR2020

  Language:Jupyter Notebook218

• Gimi818/CinemaMicroService

  A cinema management application with a ticketing system

  Language:Java4

• vladpetrovskyi/cinema-booking_project

  Cinema ticket booking service

  Language:Java85

• nguy3n47/cinema-ticket-booking

  A web application that allows users to book movie tickets, including the management page of theaters, movies, showtimes, and revenue statistics.

  Language:JavaScript102

• aakashsr/Movie-Seat-Booking

  Movie seat booking UI that allows booking of tickets including seat reservations

  Language:HTML246

• yash-patni/UML-Diagrams

  Online Movie Ticket Booking System

  1

• mpgn/BEAST-PoC

  :muscle: Proof Of Concept of the BEAST attack against SSL/TLS CVE-2011-3389 :muscle:

  Language:Python7030

• russellizadi/ssp

  Language:Python9319

• ebrahimpichka/GAT-pt

  PyTorch implementation of the Graph Attention Networks (GAT) based on the paper "Graph Attention Network" by Velickovic et al - https://arxiv.org/abs/1710.10903v3

  Language:Python293

• bifurcation/mint

  A Minimal TLS 1.3 Implementation in Go

  Language:Go22436

• tlswg/tls13-spec

  TLS 1.3 Specification

  Language:Python563159

• skovv3/Kaggle-Data-Cleaning-Challenge

  Learn professional data cleaning techniques! Data cleaning is a key part of data science, but it can be deeply frustrating. Why are some of your text fields garbled? What should you do about those missing values? Why aren’t your dates formatted correctly? How can you quickly clean up inconsistent data entry? In this five day challenge, you'll learn why you've run into these problems and, more importantly, how to fix them! In this challenge we’ll learn how to tackle some of the most common data cleaning problems so you can get to actually analyzing your data faster. We’ll work through five hands-on exercises with real, messy data and answer some of your most commonly-asked data cleaning questions. Here's a day-by-day breakdown of what we'll be learning each day: - Day 1: Handling missing values - Day 2: Data scaling and normalization - Day 3: Cleaning and parsing dates - Day 4: Character encoding errors (no more messed up text fields!) - Day 5: Fixing inconsistent data entry & spelling errors Ready to get started? Just enter your e-mail below to register. How do I know what to do each day? Every day you’ll get an email with instructions for that day’s challenge sent to the address you provide below. What if I need help? You're welcome to ask for help on the forums or in the comments section of the notebook for each day. When is the challenge? This 5-Day Challenge will run from March 26 through March 30 2018. What do I need to know to get started? This challenge will be taught in Python and assumes you have used some Python before. If you haven't, try working through the Kaggle Learn Machine Learning curriculum before you get started to get up to speed.

  Language:Jupyter Notebook116

• gordicaleksa/pytorch-GAT

  My implementation of the original GAT paper (Veličković et al.). I've additionally included the playground.py file for visualizing the Cora dataset, GAT embeddings, an attention mechanism, and entropy histograms. I've supported both Cora (transductive) and PPI (inductive) examples!

  Language:Jupyter Notebook2.4k329