rahul0082's Stars
ashishps1/awesome-low-level-design
Learn Low Level Design (LLD) and prepare for interviews using free resources.
tssovi/grokking-the-object-oriented-design-interview
poteto/hiring-without-whiteboards
⭐️ Companies that don't have a broken hiring process
nas5w/interview-guide
An opinionated, actionable guide for software engineering interviews.
jeffallen/jra-go
Automatically exported from code.google.com/p/jra-go
donnemartin/system-design-primer
Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.
InterviewReady/system-design-resources
These are the best resources for System Design on the Internet
ashishps1/awesome-system-design-resources
Learn System Design concepts and prepare for interviews using free resources.
jwasham/coding-interview-university
A complete computer science study plan to become a software engineer.
mattcone/markdown-guide
The comprehensive Markdown reference guide.
AkashSingh3031/The-Complete-FAANG-Preparation
This repository contains all the DSA (Data-Structures, Algorithms, 450 DSA by Love Babbar Bhaiya, FAANG Questions), Technical Subjects (OS + DBMS + SQL + CN + OOPs) Theory+Questions, FAANG Interview questions, and Miscellaneous Stuff (Programming MCQs, Puzzles, Aptitude, Reasoning). The Programming languages used for demonstration are C++, Python, and Java.
ashuray/InterviewRoom
Contains all important data structure and algorithms problems asked in interviews
Jonathan-Uy/CSES-Solutions
Accepted Solutions to the CSES Competitive Programming Problem Set
190050061-renu-k/Movie-Tickets-Booking-Platform
akj0811/Competitive-Programming
This repo contains various resources for beginners in the field of Competitive Programming
seanprashad/leetcode-patterns
A pattern-based approach for learning technical interview questions
facebookresearch/denoiser
Real Time Speech Enhancement in the Waveform Domain (Interspeech 2020)We provide a PyTorch implementation of the paper Real Time Speech Enhancement in the Waveform Domain. In which, we present a causal speech enhancement model working on the raw waveform that runs in real-time on a laptop CPU. The proposed model is based on an encoder-decoder architecture with skip-connections. It is optimized on both time and frequency domains, using multiple loss functions. Empirical evidence shows that it is capable of removing various kinds of background noise including stationary and non-stationary noises, as well as room reverb. Additionally, we suggest a set of data augmentation techniques applied directly on the raw waveform which further improve model performance and its generalization abilities.
maxim5/cs229-2018-autumn
All notes and materials for the CS229: Machine Learning course by Stanford University
lnishan/awesome-competitive-programming
:gem: A curated list of awesome Competitive Programming, Algorithm and Data Structure resources
sindresorhus/awesome
😎 Awesome lists about all kinds of interesting topics
wncc/CodeInQuarantine
190050061-renu-k/notify-me
codingforentrepreneurs/Try-Django
Learn Django bit by bit in this series