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%)

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

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

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

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

[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)

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

Data for the W-NUT 2020 Shared Task 2: Identification of informative COVID-19 English Tweets

Binary Image Classification in TensorFlow

Package provides javascript implementation of linear regression and logistic regression

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.

Practical ML and NLP with examples.

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).

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

Machine learning enabled dropper

Fully supervised binary classification of skin lesions from dermatoscopic images using an ensemble of diverse CNN architectures (EfficientNet-B6, Inception-V3, SEResNeXt-101, SENet-154, DenseNet-169) with multi-scale input.

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

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

A repo for NLP model Bert.