pavantanniru

pavantanniru's Stars

• ashishps1/awesome-leetcode-resources

  Awesome LeetCode resources to learn Data Structures and Algorithms and prepare for Coding Interviews.

  5.3k1.4k

• Akash1362000/Django_BlogApp

  A blog app ✍ developed using Python Django 🌐 with many features such as - View Blogs written by community 👨‍👩‍👦 | Write your own blogs by creating an account ✔ | View, Update your profile 💇‍♂️ | Upload Profile Picture 🤳 | Password reset feature 🔐

  Language:Python9222

• kying18/beginner-projects

  Language:Python364252

• krishnaik06/Data-Analyst-Roadmap

  722287

• PipedreamHQ/pipedream

  Connect APIs, remarkably fast. Free for developers.

  Language:JavaScript8.7k5.3k

• microsoft/PowerToys

  Windows system utilities to maximize productivity

  Language:C#109k6.4k

• krishnaik06/Python-Practise-Problems

  592247

• conda/conda

  A system-level, binary package and environment manager running on all major operating systems and platforms.

  Language:Python6.3k1.6k

• codebasics/py

  Repository to store sample python programs for python learning

  Language:Jupyter Notebook6.8k16.8k

• PacktPublishing/Becoming-a-Salesforce-Certified-Technical-Architect

  Becoming a Salesforce Certified Technical Architect, published by Packt

  3325

• in28minutes/roadmaps

  Roadmaps of in28minutes courses!

  24694

• thecodercoder/frontend-boilerplate

  front-end boilerplate for Sass and Gulp 4

  Language:JavaScript325175

• akshaybahadur21/Alphabet-Recognition-EMNIST

  Alphabet recognition using EMNIST dataset for humans ⚓

  Language:Python6824

• googlecreativelab/quickdraw-dataset

  Documentation on how to access and use the Quick, Draw! Dataset.

  6.1k935

• akshaybahadur21/QuickDraw

  A simple implementation of Google's Quick, Draw Project for humans. 🖌️ 🖼️

  Language:Python22274

• reduxjs/redux

  A JS library for predictable global state management

  Language:TypeScript60.8k15.3k

• rwaldron/idiomatic.js

  Principles of Writing Consistent, Idiomatic JavaScript

  24.6k3.5k

• qiushiyan/linux-command-line-cheatsheet

  a cheatsheet of common linux commands

  Language:TeX7513

• emmetio/emmet-atom

  Emmet support for Atom

  Language:CoffeeScript763181

• benoitc/gunicorn

  gunicorn 'Green Unicorn' is a WSGI HTTP Server for UNIX, fast clients and sleepy applications.

  Language:Python9.7k1.7k

• miguelgrinberg/Flask-Migrate

  SQLAlchemy database migrations for Flask applications using Alembic

  Language:Python2.3k225

• romeojeremiah/javascript-projects-for-beginners

  Repository for all (future) 100+ JavaScript projects for beginners created on JSBeginners.com.

  Language:CSS815234

• ruch798/NLP-with-Disaster-Tweets

  Language:Jupyter Notebook3

• tunguz/ML_Resources

  GitHub Repo with various ML/AI/DS resources that I find useful

  45237

• harsha-create/webdevelopment

  Language:HTML1

• ram-jay07/Code-Overflow

  Awesome Coding Problems

  1

• dataprofessor/data

  Data Sets for Machine Learning Practice

  226301

• plotly/plotly.py

  The interactive graphing library for Python :sparkles: This project now includes Plotly Express!

  Language:Python16k2.5k

• georgearun/Data-Science--Cheat-Sheet

  Cheat Sheets

  7758.7k

• yasserhessein/deep-learning-classification-mammographic-mass

  Data Set Information: Mammography is the most effective method for breast cancer screening available today. However, the low positive predictive value of breast biopsy resulting from mammogram interpretation leads to approximately 70% unnecessary biopsies with benign outcomes. To reduce the high number of unnecessary breast biopsies, several computer-aided diagnosis (CAD) systems have been proposed in the last years.These systems help physicians in their decision to perform a breast biopsy on a suspicious lesion seen in a mammogram or to perform a short term follow-up examination instead. This data set can be used to predict the severity (benign or malignant) of a mammographic mass lesion from BI-RADS attributes and the patient's age. It contains a BI-RADS assessment, the patient's age and three BI-RADS attributes together with the ground truth (the severity field) for 516 benign and 445 malignant masses that have been identified on full field digital mammograms collected at the Institute of Radiology of the University Erlangen-Nuremberg between 2003 and 2006. Each instance has an associated BI-RADS assessment ranging from 1 (definitely benign) to 5 (highly suggestive of malignancy) assigned in a double-review process by physicians. Assuming that all cases with BI-RADS assessments greater or equal a given value (varying from 1 to 5), are malignant and the other cases benign, sensitivities and associated specificities can be calculated. These can be an indication of how well a CAD system performs compared to the radiologists. Class Distribution: benign: 516; malignant: 445

  Language:Jupyter Notebook3