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k-burner's Projects

bayesianoptimization

A Python implementation of global optimization with gaussian processes.

bert-as-service

Mapping a variable-length sentence to a fixed-length vector using BERT model

chatbot_cn

基于金融-司法领域(兼有闲聊性质)的聊天机器人，其中的主要模块有信息抽取、NLU、NLG、知识图谱等，并且利用Django整合了前端展示,目前已经封装了nlp和kg的restful接口

deep-trading

Algorithmic trading with deep learning experiments

gpt_academic

为ChatGPT/GLM提供实用化交互界面，特别优化论文阅读/润色/写作体验，模块化设计，支持自定义快捷按钮&函数插件，支持Python和C++等项目剖析&自译解功能，PDF/LaTex论文翻译&总结功能，支持并行问询多种LLM模型，支持chatglm2等本地模型。兼容文心一言, moss, llama2, rwkv, claude2, 通义千问, 书生, 讯飞星火等。

handson-ml

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.

keras-text-summarization

Text summarization using seq2seq in Keras

keras-yolov3-mobilenet

I transfer the backend of yolov3 into Mobilenetv1，VGG16，ResNet101 and ResNeXt101

machinelearning

My blogs and code for machine learning. http://cnblogs.com/pinard

mask_rcnn

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

自然语言处理（nlp），闲聊机器人（检索式chatbot），BERT句向量-相似度（Sentence Similarity），文本分类（Text classify），实体提取（ner，bert+bilstm+crf），数据增强（text augment enhance），同义句同义词生成，句子主干提取（mainpart），中文汉语短文本相似度，文本特征工程，keras-http-service调用

opencv

Open Source Computer Vision Library

pangfengzi.github.io

My blog is here

pdf

数据科学方向课件&资料

pointer_summarizer

pytorch implementation of "Get To The Point: Summarization with Pointer-Generator Networks"

rktk-wxx

软考题库微信小程序 ENJOY

seq2seq

keras example of seq2seq, auto title

tensorflow-examples

TensorFlow Tutorial and Examples for Beginners with Latest APIs

trackr-cnn

TrackR-CNN baseline method for Multi-Object Tracking and Segmentation (MOTS)

transformers

Abstractive summarization using tensor2tensor model (or Transformers) without RNNs or CNNs. "Attention Is All You Need."

transummar

Transformer for abstractive summarization on cnn/daily-mail and gigawords

ugatit-pytorch

Official PyTorch implementation of U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation

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