zeigar,github

-chanlun

缠论量化，人类历史上第一个数学上可以严格证明的技术分析策略，包含严格笔，线段作为最低级别，中枢的构成，级别的扩展，第一二三类买卖点，同级别分解的，背驰的判断，区间套，需要技术合作的加微信17866148858，备注"github"。走势分解例子在readme,因海外通达信无法方便接收实时行情，用大智慧制作的走势分解程序，减轻脑力工作量。因时间有限只和有缠论技术基础的人交流。可解决比如走势递归的划分，如何同级别分解，二买三买的低点到底当下如何判断在何处，小转大的程序判断等问题。用缠论主观交易就能稳定赚钱。只有用缠论主观交易能挣钱的人，才有可能量化交易也挣钱。何次交易，本人都可以明确在当下，不知道后续走势的情况下指出其错误所在，从而在当时避免错误。如果交易能亏损很多，用亏损的钱的十分之一学习到能避免重复的亏损的问题，大大的值得了。缠论的难点在于，同级别分解如何分解，同级别分解的多义性，背驰力度的计算，小转大卖点的处理，跳空等极端走势对走势划分的影响，卖点比买点级别小的把握，中枢震荡的操作，如何规避T+1,等等。

3dbraingen

Official Pytorch Implementation of "Generation of 3D Brain MRI Using Auto-Encoding Generative Adversarial Network" (accepted by MICCAI 2019)

3dcnn-vis

Visual Explanations From Deep 3D Convolutional Neural Networks for Alzheimer's Disease Classification

3dtopoplot

acenet-for-neuroanatomy-segmentation

ACEnet: Anatomical Context-Encoding Network for Neuroanatomy Segmentation

ad_examples

A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.

alphacsc

Convolution dictionary learning for time-series

ann4brains

Artificial neural networks for brain networks

ants

Advanced Normalization Tools (ANTs)

antspy

Advanced Normalization Tools in Python

antspynet

Medical image analysis framework merging ANTsPy and deep learning

antsregistration-maget

arl-eegmodels

This is the Army Research Laboratory (ARL) EEGModels Project: A Collection of Convolutional Neural Network (CNN) models for EEG signal classification, using Keras and Tensorflow

auto-eeg-diagnosis-comparison

autogluon

AutoGluon: AutoML Toolkit for Deep Learning

automatic-ecg-diagnosis

Scripts and modules for training and testing neural network for ECG automatic classification. Companion code to the paper "Automatic diagnosis of the 12-lead ECG using a deep neural network".