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《Python统计与数据分析实战》课程代码，包含了大部分统计与非参数统计和数据分析的模型、算法。回归分析、方差分析、点估计、假设检验、主成分分析、因子分析、聚类分析、判别分析、对数线性模型、分位回归模型以及列联表分析、非参数平滑、非参数密度估计等各种非参数统计方法。

accuratebg icon accuratebg

Patient-specific blood glucose prediction using deep learning, considering the challenges of "small dataset" and "data imbalance"

awesome-cpp icon awesome-cpp

A curated list of awesome C++ (or C) frameworks, libraries, resources, and shiny things. Inspired by awesome-... stuff.

behind-the-mask---image-analytics-using-gans icon behind-the-mask---image-analytics-using-gans

While the use of Generative Adversarial Networks (GANs) has been a breakthrough in the computer vision industry, there exist multiple styles of GANs that are well-tailored to solve specific problems. Behind the mask, though sounding trivial, points to a critical use case. The situation represents the unsupervised image to image translation by discovering distinctive features from the first set and generating images belonging to the other set by learning distinctions between these two. This technique is more feasible for problems where paired images are not available. Using algorithms like Pix2pix is not viable since paired images are expensive and difficult to obtain. To tackle this problem, CycleGAN, DualGAN, and DiscoGAN provide an insight into which the models can learn the mapping from one image domain to another one with unpaired image data. But even in this case, since the problem is reconstructing human faces by removing their facial masks, which requires non-linear transformations, this is tricky. Moreover, the previously mentioned techniques also alter the background and make changes to unwanted objects as they try to create fake images through generators and discriminators. The goal is to implement an approach that not only detects discriminating factors between two sets of pictures but also generates images without altering the rest of the details and only targets specific areas of the image to change. One other technique that can be employed to address this could be to use Contrast GAN, which selects a part of an image, transforms that based on differentiating factors, and then pastes it back to the original image. However, this created an issue since the face masks used in our case had to be of the exact dimensions and identical, which was not the case. To overcome these challenges, we tried to employ an attention-based technique named AGGAN, Attention-Guided Generative Adversarial Networks, for image translation that does not require additional models/parameters to alter a specific part of the image. The AGGAN comprises two generators and two discriminators, like CycleGAN. Two attention-guided generators in AGGAN have built-in attention modules, which can disentangle the discriminative semantic object and the unwanted part by producing an attention mask and a content mask. The underlying image is fused with these masks to create quality fake images. We also consider additional losses to reduce the variance and make the related images pixel consistent. We think of a more sophisticated network by applying two possible subnets to identify the attention and content masks. To avoid omitting any details, the network employs two attention masks, one for the foreground and one for the background, so that the foreground can be better learned, and the background can be preserved. Also, in this case, the generative content mask is introduced to multiple types of facial masks to identify a broad spectrum of them and effectively remove them and create a more decadent generation space. To obtain high-quality unmasked images, we aim and expect to translate masked images to unmasked ones that can be employed on various faces with different skin colors and expressions.

bert_in_keras icon bert_in_keras

在Keras下微调Bert的一些例子；some examples of bert in keras

bilibili-plus icon bilibili-plus

课程视频、PPT和源代码：侯捷C++系列；台大郭彦甫MATLAB

bytetrack icon bytetrack

ByteTrack: Multi-Object Tracking by Associating Every Detection Box

c- icon c-

A Detailed Cplusplus Concurrency Tutorial 《C++ 并发编程指南》

c-c- icon c-c-

程序员相关电子书资料免费分享，欢迎关注个人微信公众号：程序员编程指南

cpp-httplib icon cpp-httplib

A C++ header-only HTTP/HTTPS server and client library

cpp_new_features icon cpp_new_features

2021年最新整理， C++ 学习资料，含C++ 11 / 14 / 17 / 20 / 23 新特性、入门教程、推荐书籍、优质文章、学习笔记、教学视频等

cuda-image-processing icon cuda-image-processing

Developing a complete set of GPU-accelerated image processing tools, including convolution and morphology

cv-detect-robot icon cv-detect-robot

yolov5+yolox+tensorRT+ros+deepstream+jetson+nano+TX2+NX High-performance deployment(高性能部署)

ddad icon ddad

扩散学习的工业瑕疵检测

deeppavlov icon deeppavlov

An open source library for deep learning end-to-end dialog systems and chatbots.

deltacv icon deltacv

A high performance library for image processing

dirent icon dirent

C/C++ library for retrieving information on files and directories

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