Fast and customizable framework for automatic ML model creation (AutoML)
A multi-platform GUI for bit-based analysis, processing, and visualization
Credit risk analysis for credit card applicants
Binary and Categorical Focal loss implementation in Keras.
Code for Java Deep Learning Cookbook
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 pipelinesβ¦
Text classification with Convolution Neural Networks on Yelp, IMDB & sentence polarity dataset v1.0
Detecting Autism Spectrum Disorder in Children With ComputerΒ Vision - Adapting facial recognition models to detect Autism SpectrumΒ Disorder
1st place solution of RSNA Screening Mammography Breast Cancer Detection competition on Kaggle: https://www.kaggle.com/competitions/rsna-breast-cancer-detection
Multi-modal AI-generated content detection: image, video, and audio. Benchmarks, training code (DINOv2, DINOv3, ReStraV, BreathNet), and evaluation pipeline for real vs. synthetic classification with calibration-aware metrics.
2017-CCF-BDCI-δΌδΈη»θ₯ιεΊι£ι©ι’ζ΅οΌ9th/569 (Top 1.58%)
Implementation of Focal Loss (Lin et al., 2017, Facebook AI Research) for handling class imbalance by focusing learning on hard, misclassified examples.
π§Ή Formerly for binary classification with noisy labels. Replaced by cleanlab.
Simple Transparent End-To-End Automated Machine Learning Pipeline for Supervised Learning in Tabular Binary Classification Data
The binclass-tools package contains a set of Python wrappers and interactive plots that facilitate the analysis of binary classification problems.
2018 - Kaggle - TalkingData AdTracking Fraud Detection Challenge: Silver medal (ιΆη)
[ICMLSC 2018] On Breast Cancer Detection: An Application of Machine Learning Algorithms on the Wisconsin Diagnostic Dataset
A memory efficient GBDT on adaptive distributions. Much faster than LightGBM with higher accuracy. Implicit merge operation.
A set of deep learning models for FRB/RFI binary classification.
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