nikvaessen's Stars
denoland/deno
A modern runtime for JavaScript and TypeScript.
floodsung/Deep-Learning-Papers-Reading-Roadmap
Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!
kuchin/awesome-cto
A curated and opinionated list of resources for Chief Technology Officers, with the emphasis on startups
Lightning-AI/pytorch-lightning
Pretrain, finetune ANY AI model of ANY size on multiple GPUs, TPUs with zero code changes.
viraptor/reverse-interview
Questions to ask the company during your interview
sebastianruder/NLP-progress
Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.
dennybritz/reinforcement-learning
Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.
speechbrain/speechbrain
A PyTorch-based Speech Toolkit
charlax/engineering-management
A collection of inspiring resources related to engineering management and tech leadership
fpereiro/backendlore
How I write backends
JackHack96/dell-xps-9570-ubuntu-respin
Collection of scripts and tweaks to adapt Ubuntu running smooth on Dell XPS 15 9570.
yukinarit/pyserde
Yet another serialization library on top of dataclasses, inspired by serde-rs.
thuml/awesome-multi-task-learning
2024 up-to-date list of DATASETS, CODEBASES and PAPERS on Multi-Task Learning (MTL), from Machine Learning perspective.
jitsi/jiwer
Evaluate your speech-to-text system with similarity measures such as word error rate (WER)
nikvaessen/w2v2-speaker
Research code for the paper "Fine-tuning wav2vec2 for speaker recognition" found at https://arxiv.org/abs/2109.15053
toddkarin/global-land-mask
Check whether a lat/lon point is on land for any point on earth
asappresearch/sew
vishaljadav24/react-native-hide-show-password-input
React-Native Hide Show Password InputText Component
mfherbst/awk-course
Material for the "Introduction to awk programming" course at Heidelberg University
nikvaessen/w2v2-speaker-few-samples
Research code for the paper "Training speaker recognition systems with limited data" at https://arxiv.org/abs/2203.14688
Loes5307/VocalAdversary2022