머신러닝 · GitHub

A C library for product recommendations/suggestions using collaborative filtering (CF)

Hunting for passwords with deep learning

A Python framework for program synthesis with a focus on Automated Machine Learning.

Pytorch implementation of the paper "Optimization as a Model for Few-Shot Learning"

[ICLR'23 Oral] Universal Few-shot Learning of Dense Prediction Tasks with Visual Token Matching

Deep-Learning based CTR models implemented by PyTorch

Detection of Accounting Anomalies using Deep Autoencoder Neural Networks - A lab we prepared for NVIDIA's GPU Technology Conference 2018 that will walk you through the detection of accounting anomalies using deep autoencoder neural networks. The majority of the lab content is based on Jupyter Notebook, Python and PyTorch.

Keras code and weights files for the VGG16-places365 and VGG16-hybrid1365 CNNs for scene classification

Python package for implementing a number of Machine Learning, Randomized Optimization and SEarch algorithms.

Survival analsyis and time-to-failure predictive modeling using Weibull distributions and Recurrent Neural Networks in Keras

Convert ONNX model graph to Keras model format.

pytorch implementation of scene change detection

Official code for CVPR 2022 (Oral) paper "Deep Visual Geo-localization Benchmark"

A brain-inspired version of generative replay for continual learning with deep neural networks (e.g., class-incremental learning on CIFAR-100; PyTorch code).

Simple pytorch unofficial implement of paper: Learning Discriminative Features with Multiple Granularities for Person Re-Identification

Verify the authenticity of handwritten signatures through digital image processing and neural networks. ✍️

pyTorch implementation of Spectral Normalization for Generative Adversarial Networks

This repo implements PyramidBox with pytorch

🏃 Implementation of Using Fast Weights to Attend to the Recent Past.

Learning Machine Learning with TensorFlow

Neural Networks and the Chomsky Hierarchy

DeepConsensus uses gap-aware sequence transformers to correct errors in Pacific Biosciences (PacBio) Circular Consensus Sequencing (CCS) data.