Spacenet building detectionProject to train/test convolutional neural networks to extract buildings from SpaceNet satellite imageries.
Stars: ✭ 83 (-1.19%)
Attention is all you needTransformer of "Attention Is All You Need" (Vaswani et al. 2017) by Chainer.
Stars: ✭ 303 (+260.71%)
Alpha Zero GeneralA clean implementation based on AlphaZero for any game in any framework + tutorial + Othello/Gobang/TicTacToe/Connect4 and more
Stars: ✭ 2,617 (+3015.48%)
CoronabrSérie histórica dos dados sobre COVID-19, a partir de informações do Ministério da Saúde
Stars: ✭ 83 (-1.19%)
Tensorflow DemoLocal AI demo and distributed AI demo using TensorFlow
Stars: ✭ 82 (-2.38%)
Unsupervised anomaly detectionA Notebook where I implement differents anomaly detection algorithms on a simple exemple. The goal was just to understand how the different algorithms works and their differents caracteristics.
Stars: ✭ 82 (-2.38%)
Covid19 DataCOVID-19 workflows and datasets.
Stars: ✭ 84 (+0%)
Nbconfluxnbconflux converts Jupyter Notebooks to Atlassian Confluence pages
Stars: ✭ 82 (-2.38%)
GancsCompressed Sensing MRI based on Deep Generative Adversarial Network
Stars: ✭ 83 (-1.19%)
ImageclassificationDeep Learning: Image classification, feature visualization and transfer learning with Keras
Stars: ✭ 83 (-1.19%)
LogomakerSoftware for the visualization of sequence-function relationships
Stars: ✭ 83 (-1.19%)
Nasnet KerasKeras implementation of NASNet-A
Stars: ✭ 82 (-2.38%)
Airflow projectscaffold of Apache Airflow executing Docker containers
Stars: ✭ 84 (+0%)
Cs231nStanford cs231n'18 assignment
Stars: ✭ 82 (-2.38%)
Yolo resnetImplementing YOLO using ResNet as the feature extraction network
Stars: ✭ 82 (-2.38%)
MlMachine learning projects, often on audio datasets
Stars: ✭ 83 (-1.19%)
Credit card fraudThis repository includes the code used in my corresponding Medium post.
Stars: ✭ 82 (-2.38%)
RsnLearning to Exploit Long-term Relational Dependencies in Knowledge Graphs, ICML 2019
Stars: ✭ 83 (-1.19%)
Esdastatistics and classes for exploratory spatial data analysis
Stars: ✭ 83 (-1.19%)
TorchtexttutorialA short tutorial for Torchtext, the NLP-specific add-on for Pytorch.
Stars: ✭ 83 (-1.19%)
Neural Networksbrief introduction to Python for neural networks
Stars: ✭ 82 (-2.38%)
Carvana ChallengeMy repository for the Carvana Image Masking Challenge
Stars: ✭ 83 (-1.19%)
Python script Manual《Python工具代码速查手册》是我们的python培训教材,主要面向数据分析方向。其中包含了python的常用总结性操作,使用jupyter notebook,利用markdown和script结果对常用操作进行总结,包括了使用方式和脚本。之所以使用notebook形式是可以方便大家编辑,方便大家形成自己的总结笔记。当然各位有更好的操作建议也欢迎向我们团队分享~
Stars: ✭ 84 (+0%)
PyeprPowerful, automated analysis and design of quantum microwave chips & devices [Energy-Participation Ratio and more]
Stars: ✭ 81 (-3.57%)
Onnx ChainerAdd-on package for ONNX format support in Chainer
Stars: ✭ 83 (-1.19%)
ArticlesPapers I read
Stars: ✭ 82 (-2.38%)
Pulmonary Nodules SegmentationTianchi medical AI competition [Season 1]: Lung nodules image segmentation of U-Net. U-Net训练基于卷积神经网络的肺结节分割器
Stars: ✭ 84 (+0%)
Fonduer TutorialsA collection of simple tutorials for using Fonduer
Stars: ✭ 82 (-2.38%)
Sequence JacobianInteractive guide to Auclert, Bardóczy, Rognlie, and Straub (2019): "Using the Sequence-Space Jacobian to Solve and Estimate Heterogeneous-Agent Models".
Stars: ✭ 82 (-2.38%)
Deepembeding图像检索和向量搜索,similarity learning,compare deep metric and deep-hashing applying in image retrieval
Stars: ✭ 83 (-1.19%)
H3 Py NotebooksJupyter notebooks for h3-py, a hierarchical hexagonal geospatial indexing system
Stars: ✭ 82 (-2.38%)
PylbmNumerical simulations using flexible Lattice Boltzmann solvers
Stars: ✭ 83 (-1.19%)
DareblopyData Reading Blocks for Python
Stars: ✭ 82 (-2.38%)
EconmlALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
Stars: ✭ 1,238 (+1373.81%)
GraphlogAPI for accessing the GraphLog dataset
Stars: ✭ 82 (-2.38%)
Mlcourse生命情報の機械学習入門(新学術領域「先進ゲノム支援」中級講習会資料)
Stars: ✭ 83 (-1.19%)
Kaggle QuoraKaggle Quora Questions Pairs Competition
Stars: ✭ 84 (+0%)
CytokitMicroscopy Image Cytometry Toolkit
Stars: ✭ 84 (+0%)