zcdliuwei
Machine Learning Research Engineer Master of Probability and Statistics
Small DesignHangZhou in China
Pinned Repositories
DataAugmentationForObjectDetection
Data Augmentation For Object Detection
deep_learning_object_detection
A paper list of object detection using deep learning.
DeepLabV3_Plus-Tensorflow2.0
DeepLabV3+ implemented in TensorFlow2.0
DeepLearningExamples
Deep Learning Examples
handson-ml2
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
imgaug
Image augmentation for machine learning experiments.
Mask_RCNN
Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow
tensorflow-yolov4-tflite
YoloV4 Implemented in Tensorflow 2.0. Convert .weights to .pb and .tflite format for tensorflow and tensorflow lite.
transformers
🤗 Transformers: State-of-the-art Natural Language Processing for TensorFlow 2.0 and PyTorch.
yolov3-tf2
YoloV3 Implemented in Tensorflow 2.0
zcdliuwei's Repositories
zcdliuwei/DeepLabV3_Plus-Tensorflow2.0
DeepLabV3+ implemented in TensorFlow2.0
zcdliuwei/imgaug
Image augmentation for machine learning experiments.
zcdliuwei/yolov3-tf2
YoloV3 Implemented in Tensorflow 2.0
zcdliuwei/a-PyTorch-Tutorial-to-Object-Detection
SSD: Single Shot MultiBox Detector | a PyTorch Tutorial to Object Detection
zcdliuwei/anomaly-detection-resources
Anomaly detection related books, papers, videos, and toolboxes
zcdliuwei/Awesome-Tensorflow2
基于Tensorflow2开发的优秀扩展包及项目
zcdliuwei/bert-for-tf2
A Keras TensorFlow 2.0 implementation of BERT, ALBERT and adapter-BERT.
zcdliuwei/Brain-Tumor-Segmentation
Attention-Guided Version of 2D UNet for Automatic Brain Tumor Segmentation
zcdliuwei/d2l-en
Dive into Deep Learning: an interactive deep learning book with code, math, and discussions, based on the NumPy interface.
zcdliuwei/d2l-zh
《动手学深度学习》:面向中文读者、能运行、可讨论。英文版即伯克利“深度学习导论”教材。
zcdliuwei/Deep-Learning-for-Recommendation-Systems
This repository contains Deep Learning based articles , paper and repositories for Recommender Systems
zcdliuwei/deep-learning-resources
由淺入深的深度學習資源 Collection of deep learning materials for everyone
zcdliuwei/DeepRecommender
Deep learning for recommender systems
zcdliuwei/Dive-into-DL-PyTorch
本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为PyTorch实现。
zcdliuwei/Dive-into-DL-TensorFlow2.0
本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为TensorFlow 2.0实现,项目已得到李沐老师的同意
zcdliuwei/eat_tensorflow2_in_30_days
Tensorflow2.0 is delicious, just eat it!
zcdliuwei/HCMVcc_MaskRCNN
Mask-RCNN for the classification and segmentation of HCMV infection (Matterport, Tensorflow 2.0)
zcdliuwei/Image-processing-algorithm
paper implement
zcdliuwei/Introduction-NLP
HanLP作者的新书《自然语言处理入门》详细笔记!业界良心之作,书中不是枯燥无味的公式罗列,而是用白话阐述的通俗易懂的算法模型。从基本概念出发,逐步介绍中文分词、词性标注、命名实体识别、信息抽取、文本聚类、文本分类、句法分析这几个热门问题的算法原理与工程实现。
zcdliuwei/keras-yolo3
A Keras implementation of YOLOv3 (Tensorflow backend)
zcdliuwei/mit-deep-learning
Tutorials, assignments, and competitions for MIT Deep Learning related courses.
zcdliuwei/models
Models and examples built with TensorFlow
zcdliuwei/TensorFlow-2.x-Tutorials
TensorFlow 2.x version's Tutorials and Examples, including CNN, RNN, GAN, Auto-Encoders, FasterRCNN, GPT, BERT examples, etc. TF 2.0版入门实例代码,实战教程。
zcdliuwei/tensorflow-serving-yolov3
主要对原tensorflow版本算法进行了网络修改,显示调整,数据处理等细节优化,详细说明了 从本地训练到serving端部署yolov3的整个流程,训练了Visdrone2019无人机数据集, 准确率 较高, 训练工业检测数据集(非80类中的一类),mAP为97.51,FPS在1080上测试15-20帧!
zcdliuwei/tensorflow-yolov3
🔥 pure tensorflow Implement of YOLOv3 with support to train your own dataset
zcdliuwei/TensorFlow2.0-Examples
🙄 difficult algorithm, simple code.
zcdliuwei/tensorflow2_tutorials_chinese
tensorflow2中文教程,持续更新(当前版本:tensorflow2.0),tag: tensorflow 2.0 tutorials
zcdliuwei/tensorpack
A Neural Net Training Interface on TensorFlow, with focus on speed + flexibility
zcdliuwei/tf2rl
TensorFlow2.0 Reinforcement Learning
zcdliuwei/Tips-of-Feature-engineering
A feature engineering kit for each issue, to give you a deeper and deeper understanding of the work of feature engineering!