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optnet

OptNet: Differentiable Optimization as a Layer in Neural Networks

optuna

A hyperparameter optimization framework

optunity

optimization routines for hyperparameter tuning

optvoltrading

option volatility trading strategy

orange3-associate

🍊 :package: Frequent itemsets and association rules mining for Orange 3.

orb_slam

A Versatile and Accurate Monocular SLAM

order-embedding

Implementation of caption-image retrieval from the paper "Order-Embeddings of Images and Language"

oreilly-pytorch

Introductory PyTorch Tutorials

orf-saffari

My implementation of Online Random Forest by Saffari

orfpy

Online Random Forests (Saffari) for Python

origami

:unlock: :key: :closed_lock_with_key: Origami: Artificial Intelligence as a Service

orthnet

TensorFlow and PyTorch layers for generating Orthogonal Polynomials

ost-hmd-calibration

A Calibration Method for Optical See-through Head-mounted Displays with a Depth Camera

osvos-caffe

One-Shot Video Object Segmentation

osvos-pytorch

PyTorch implementation of One-Shot Video Object Segmentation (OSVOS)

osvos-tensorflow

One-Shot Video Object Segmentation

othogonalwn

This project is the Torch implementation of our accepted AAAI 2018 paper : orthogonal weight normalization method for solving orthogonality constraints over Steifel manifold in deep neural networks (arXiv:1709.06079 )

outlier-detection-algorithm---isolation-forests

BNP Paribas Kaggle Data Set Data source: https://www.kaggle.com/c/bnp-paribas-cardif-claims-management Outlier Detection- Ensemble unsupervised learning method - Isolation Forest The isolation algorithm is an unsupervised machine learning method used to detect abnormal anomalies in data such as outliers. This is once again a randomized & recursive partition of the training data in a tree structure. The number of sub samples and tree size is specified and tuned appropriately. The distance to the outlier is averaged calculating an anomaly detection score: 1 = outlier 0 = close to zero are normal data.

own_data_cnn_implementation_keras

A complete tutorial on using own dataset to train a CNN from scratch in Keras (TF & Theano Backend)-

p-gnn

Position-aware Graph Neural Networks

p-multimodal-dataset-toolbox

p2c

Photo-to-Caricature Translation

p300net

Implementation of a stateful Long Short-Term Memory (LSTM) Network for modeling event-related P300 EEG data.

pace2017

A step-by-step Keras implementation of PACE (Preference And Context Embedding) described in our KDD 2017 paper.

packnet

Code for PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning

pacnet

Pixel-Adaptive Convolutional Neural Networks (CVPR '19)

paddle

PArallel Distributed Deep LEarning

paddle-mobile

This research aims at simply deploying deeplearning on mobile and embedded devices, with low complexity and high speed. old name mobile deep learning.

paddlerec

Recommendation Algorithm大规模推荐算法库，包含推荐系统经典及最新算法LR、Wide&Deep、DSSM、TDM、MIND、Word2Vec、Bert4Rec、DeepWalk、SSR、GRU4Rec、Youtube_dnn、NCF、GNN、FM、FFM、DeepFM、DCN、DIN、DIEN、DLRM、MMOE、PLE、ESMM、MAML、xDeepFM、DeepFEFM、NFM、AFM、RALM、DMR、GateNet、NAML、DIFM、Deep Crossing、PNN、BST、AutoInt、FGCNN、FLEN、Fibinet、ListWise等，包含经典推荐系统数据集criteo 、movielens等

pag

Plan, Attend, Generate: Planning for Sequence-to-Sequence Models