autosklearn-zeroconf is a fully automated binary classifier. It is based on the AutoML challenge winner auto-sklearn. Give it a dataset with known outcomes (labels) and it returns a list of predicted outcomes for your new data. It even estimates the precision for you! The engine is tuning massively parallel ensemble of machine learning pipelines for best precision/recall.

Code for Java Deep Learning Cookbook

Binary and Categorical Focal loss implementation in Keras.

Text classification with Convolution Neural Networks on Yelp, IMDB & sentence polarity dataset v1.0

2017-CCF-BDCI-企业经营退出风险预测：9th/569 (Top 1.58%)

🧹 Formerly for binary classification with noisy labels. Replaced by cleanlab.

2018 - Kaggle - TalkingData AdTracking Fraud Detection Challenge: Silver medal (银牌)

2018-腾讯广告算法大赛-相似人群拓展(初赛)：10th/1563 (Top 0.64%)

A memory efficient GBDT on adaptive distributions. Much faster than LightGBM with higher accuracy. Implicit merge operation.

[ICMLSC 2018] On Breast Cancer Detection: An Application of Machine Learning Algorithms on the Wisconsin Diagnostic Dataset

2018-JData-联通-基于移动网络通讯行为的风险用户识别：Baseline 0.77

Online machine learning algorithms (based on OLL C++ library)

Package provides javascript implementation of linear regression and logistic regression

Malicious URL detector using keras recurrent networks and scikit-learn classifiers

Binary Image Classification in TensorFlow

2018-ICC-精品旅行服务成单预测：优胜奖

A set of deep learning models for FRB/RFI binary classification.

American Express and Analytics Vidhya presents “AmExpert”. An amazing opportunity to showcase your analytical abilities and talent.

34th (Top 3%) Rank Solution for LTFS Data Science FinHack ( ML Hackathon).

Machine learning enabled dropper

An implementation of the L2-SVM for breast cancer detection using the Wisconsin diagnostic dataset.

Practical ML and NLP with examples.

R package for automatic hyper parameter tuning and ensembles with deep learning, gradient boosting machines, and random forests. Powered by h2o.

A repo for NLP model Bert.

This model is designed to augment data, train the CNN, and, test the performance.

Solution for ENS - Societe Generale Challenge (1st place).

Predicting the Stage of Breast Cancer - M (Malignant) and B (Benign) using different Machine learning models and comparing their performance.