sprinterzzj Goto Github PK

kaggle-quora-question-pairs

Kaggle：Quora Question Pairs, 4th/3396 (https://www.kaggle.com/c/quora-question-pairs)

kaggle-understanding-clouds

Code for 1st place solution in Understanding Clouds from Satellite Images Challenge.

kaggle_crowdflower

1st Place Solution for Search Results Relevance Competition on Kaggle (https://www.kaggle.com/c/crowdflower-search-relevance)

kaggle_homedepot

Turing Test's Solution for Home Depot Product Search Relevance Competition on Kaggle (https://www.kaggle.com/c/home-depot-product-search-relevance)

kaggle_public

阿水的数据竞赛开源分支

kaggle_rsna2019_3rd_solution

kaggle_talkingdata

11th Solution of Kaggle TalkingData AdTracking Fraud Detection Challenge

kaldi

This is the official location of the Kaldi project.

kaos

open source platform for simplifying machine learning deployment

kapacitor

Open source framework for processing, monitoring, and alerting on time series data

kbpedia

KBPedia Knowledge Graph & Knowledge Ontology (KKO)

kdepy

Kernel Density Estimation in Python

kedro

A Python library for building robust production-ready data and analytics pipelines

kepler.gl

Kepler.gl is a powerful open source geospatial analysis tool for large-scale data sets.

keras-adabound

AdaBound optimizer in Keras

keras-self-attention

Attention mechanism for processing sequential data that considers the context for each timestamp.

keras-tuner

Hyperparameter tuning for humans

keras-yolov3-mobilenet

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

keras-yolov4

supports training, at least 41.1% mAP.支持训练，至少41.1%mAP。少数的给出精度的复现。

kfr

Fast, modern C++ DSP framework, FFT, Sample Rate Conversion, FIR/IIR/Biquad Filters (SSE, AVX, AVX-512, ARM NEON)

kissfft

a Fast Fourier Transform (FFT) library that tries to Keep it Simple, Stupid

lab

Organize Machine Learning Projects

landmark2019-1st-and-3rd-place-solution

The 1st Place Solution of the Google Landmark 2019 Retrieval Challenge and the 3rd Place Solution of the Recognition Challenge.

lane-detection-using-deep-learning

Autonomous self-driving is in the trend for implementing it in our real life to remove all the hassles and accidents. Modern-day transport has come a long way but still far away from perfection and all-around safety. Lane Detection is a concept of demarcating lanes on the roads while the vehicle is moving. It has the capability of changing the vehicular movements on road, making them more organized and safe. This leap could provide for driver carelessness and avoid a lot of mishaps on the roads. Ride-hailing services like Uber and Ola can use them to monitor drivers and rate them based on driving skills. We have designed and trained a deep Convolutional Network model from scratch for lane detection since a CNN based model is known to work best for image datasets. We have used BDD100k dataset for training and testing for our model. We have used various metrics values for hyper-parameters tuning and took the ones which gave the best result. The training is done on Supercomputer NVIDIA-DGX V100. Idea By: Aditya Sharma, Microsoft

larq

An Open-Source Deep Learning Library for Training Binarized Neural Networks

latent_ode

Code for "Latent ODEs for Irregularly-Sampled Time Series" paper

learn_python3_spider

python爬虫教程系列、从0到1学习python爬虫，包括浏览器抓包，手机APP抓包，各种爬虫涉及的模块的使用，如：requests、beautifulSoup、selenium、appium、scrapy等，以及IP代理，验证码识别，Mysql，MongoDB数据库的python使用，多线程多进程爬虫的使用，css 爬虫加密逆向破解，JS爬虫逆向，爬虫项目实战实例等

learning

Becoming 1% better at Data Science everyday

learnpython

以撸代码的形式学习Python

lee-slam-source

SLAM 开发学习资源与经验分享