Official style files for papers submitted to venues of the Association for Computational Linguistics

总结梳理自然语言处理工程师(NLP)需要积累的各方面知识，包括面试题，各种基础知识，工程能力等等，提升核心竞争力

TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)

深度学习模型在各大公司实战落地细节解读：主要是通过阅读各种实战文章，梳理模型落地的工程细节，涉及到搜索/推荐/自然语言处理。

an Open Course Platform for Stanford CS224n (2020 Winter)

刷算法全靠套路，认准 labuladong 就够了！English version supported! Crack LeetCode, not only how, but also why.

机器学习算法的公式推导以及numpy实现

CLUENER2020 中文细粒度命名实体识别 Fine Grained Named Entity Recognition

All Algorithms implemented in Java

🤗 Transformers: State-of-the-art Natural Language Processing for TensorFlow 2.0 and PyTorch.

机器人视觉 移动机器人 VS-SLAM ORB-SLAM2 深度学习目标检测 yolov3 行为检测 opencv PCL 机器学习 无人驾驶

[CVPR 2020] GAN Compression: Efficient Architectures for Interactive Conditional GANs

[CVPR2020] GhostNet: More Features from Cheap Operations

A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.

RTSeg: Real-time Semantic Segmentation Comparative Study

A curated list of neural network pruning resources.

LeetCode Solutions: A Record of My Problem Solving Journey.( leetcode题解，记录自己的leetcode解题之路。)

Code for paper " AdderNet: Do We Really Need Multiplications in Deep Learning?"

Tutorial: Graph Neural Networks for Natural Language Processing at EMNLP 2019 and CODS-COMAD 2020

Minimal PyTorch implementation of YOLOv3

fastNLP: A Modularized and Extensible NLP Framework. Currently still in incubation.

Pytorch implementation of our CVPR'2020 (Oral) -- HRank: Filter Pruning using High-Rank Feature Map

该仓库主要记录 NLP 算法工程师相关的面试题

Demonstrate all the questions on LeetCode in the form of animation.（用动画的形式呈现解LeetCode题目的思路）

Minimal and clean examples of machine learning algorithms implementations

tensorflow2中文教程，持续更新(当前版本:tensorflow2.0)，tag: tensorflow 2.0 tutorials

Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep lear…

PyTorch 官方中文教程包含 60 分钟快速入门教程，强化教程，计算机视觉，自然语言处理，生成对抗网络，强化学习。欢迎 Star，Fork！

YSDA course in Natural Language Processing