Pinned Repositories
-offer-python-
剑指offer（python版）
AerialDetection
AI-Competition-Collections
AI比赛经验帖子 & 训练和测试技巧帖子 集锦（收集整理各种人工智能比赛经验帖）
ai-edu
AI education materials for Chinese students, teachers and IT professionals.
Ai-Learn
人工智能学习路线图，整理近200个实战案例与项目，免费提供配套教材，零基础入门，就业实战！包括：Python，数学，机器学习，数据分析，深度学习，计算机视觉，自然语言处理，PyTorch tensorflow machine-learning,deep-learning data-analysis data-mining mathematics data-science artificial-intelligence python tensorflow tensorflow2 caffe keras pytorch algorithm numpy pandas matplotlib seaborn nlp cv等热门领域
Algorithm_Interview_Notes-Chinese
2018/2019/校招/春招/秋招/算法/机器学习(Machine Learning)/深度学习(Deep Learning)/自然语言处理(NLP)/C/C++/Python/面试笔记
AlgorithmsByPython
算法/数据结构/Python/剑指offer/机器学习/leetcode
ANPR
licence plate detection and recognition
ASFF
yolov3 with mobilenet v2 and ASFF
Kaggle-Ensemble-Guide
Code for the Kaggle Ensembling Guide Article on MLWave
zzdxlee's Repositories
zzdxlee/AI-Competition-Collections
AI比赛经验帖子 & 训练和测试技巧帖子 集锦（收集整理各种人工智能比赛经验帖）
zzdxlee/ASR_Course_Homework
分享在深蓝学院《语音识别：从入门到精通》第一期课程学习过程中完成的课后作业，供参考。
zzdxlee/bnf_cnn_qbe-std
Query by example spoken term detection using bottleneck features and a convolutional neural network
zzdxlee/brocolli
pytorch 2 caffe
zzdxlee/CCPD
[ECCV 2018] CCPD: a diverse and well-annotated dataset for license plate detection and recognition
zzdxlee/CPlusPlusThings
C++那些事
zzdxlee/CS-Books
📚 计算机技术类书籍 PDF 最强总结
zzdxlee/DeepLearning-500-questions
深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系scutjy2015@163.com 版权所有，违权必究 Tan 2018.06
zzdxlee/fucking-algorithm
刷算法全靠套路，认准 labuladong 就够了！English version supported! Crack LeetCode, not only how, but also why.
zzdxlee/HyperLPR
基于深度学习高性能中文车牌识别 High Performance Chinese License Plate Recognition Framework.
zzdxlee/HyperLPR-Meow
HyperLPR识别图片和视频中的车牌
zzdxlee/Interview-for-Algorithm-Engineer
【三年面试五年模拟】算法工程师秘籍。AIGC、传统深度学习、自动驾驶、机器学习、计算机视觉、自然语言处理、图像处理、元宇宙、SLAM等AI行业面试笔试经验分享
zzdxlee/interview-questions
根据超过 1500 篇真实面经整理的腾讯，阿里，字节跳动，Shopee，美团，滴滴高频面试题
zzdxlee/keyword-spotting-research-datasets
zzdxlee/leetcode-master
LeetCode 刷题攻略：配思维导图，各个类型的经典题目刷题顺序、经典算法模板，以及详细图解和视频题解。这里精选的题目都不是孤立的，而是由浅入深一脉相承的，相信只要按照刷题攻略上的顺序来学习，一定会有所收获！给个star支持一下吧！
zzdxlee/LeetcodeTop
汇总各大互联网公司容易考察的高频leetcode题🔥
zzdxlee/License-Plate-Detect-Recognition-via-Deep-Neural-Networks-accuracy-up-to-99.9
works in real-time with detection and recognition accuracy up to 99.8% for Chinese license plates: 100 ms/plate
zzdxlee/Object-Detection-and-Tracking
YOLO & RCNN Object Detection and Multi-Object Tracking
zzdxlee/objectdetection_script
一些关于目标检测的脚本的改进思路代码，详细请看readme.md
zzdxlee/prepare_detection_dataset
convert dataset to coco/voc format
zzdxlee/python-data-structure-cn
problem-solving-with-algorithms-and-data-structure-using-python 中文版
zzdxlee/ReID_tutorial_slides
《深度学习与行人重识别》课程课件
zzdxlee/rnn_kws
Keyword spotting using RNNs + Edit distance
zzdxlee/SegFeat
Phoneme Boundary Detection using Learnable Segmental Features (ICASSP 2020)
zzdxlee/shennong
A Python toolbox for speech features extraction
zzdxlee/snowboy
DNN based hotword and wake word detection toolkit
zzdxlee/Speech_Signal_Processing_and_Classification
Front-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can discriminate between utterances of a subject suffering from say vocal fold paralysis and utterances of a healthy subject.The mathematical modeling of the speech production system in humans suggests that an all-pole system function is justified [1-3]. As a consequence, linear prediction coefficients (LPCs) constitute a first choice for modeling the magnitute of the short-term spectrum of speech. LPC-derived cepstral coefficients are guaranteed to discriminate between the system (e.g., vocal tract) contribution and that of the excitation. Taking into account the characteristics of the human ear, the mel-frequency cepstral coefficients (MFCCs) emerged as descriptive features of the speech spectral envelope. Similarly to MFCCs, the perceptual linear prediction coefficients (PLPs) could also be derived. The aforementioned sort of speaking tradi- tional features will be tested against agnostic-features extracted by convolu- tive neural networks (CNNs) (e.g., auto-encoders) [4]. The pattern recognition step will be based on Gaussian Mixture Model based classifiers,K-nearest neighbor classifiers, Bayes classifiers, as well as Deep Neural Networks. The Massachussets Eye and Ear Infirmary Dataset (MEEI-Dataset) [5] will be exploited. At the application level, a library for feature extraction and classification in Python will be developed. Credible publicly available resources will be 1used toward achieving our goal, such as KALDI. Comparisons will be made against [6-8].
zzdxlee/SpeechAlgorithms
Code of my WeChat Offical Account
zzdxlee/transformers-code
手把手带你实战 Huggingface Transformers 课程视频同步更新在B站与YouTube
zzdxlee/vehicle_counting_tensorflow
"MORE THAN VEHICLE COUNTING!" This project provides prediction for speed, color and size of the vehicles with TensorFlow Object Counting API.