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fm-model-kg-link-prediction

A Model derived from Factorization Machine for learning the latent representations for entities and relations in Knowledge Graphs.

fm_dmlc

Distributed FM and LR based on Rabit with Lbfgs

fm_ftrl

Hashed Factorization Machine with Follow The Regularized Leader for Kaggle Avazu Click-Through Rate Competition

fm_tf

fmfn-bayesianoptimization

fmg

KDD17_FMG

fmin

Unconstrained function minimization in Javascript

fmrmr

MRMR feature selection

fmrt

A pytorch implementation of FMRT (Certifiable Robustness to Discrete Adversarial Perturbations for Factorization Machines) [SIGIR20]

fmwr

FMwR: A Library of Factorization Machines in R Based on libfm

foci

:exclamation: This is a read-only mirror of the CRAN R package repository. FOCI — Feature Ordering by Conditional Independence

focus

folder-structure-conventions

Folder / directory structure options and naming conventions for software projects

folia

FoLiA: Format for Linguistic Annotation - FoLiA is a rich XML-based annotation format for the representation of language resources (including corpora) with linguistic annotations. A wide variety of linguistic annotations are support, making FoLiA a useful format for NLP tasks and data interchange. Note that the actual Python library for processing FoLiA is implemented as part of PyNLPl, this contains higher-level tools that use the library as well as the full documentation, validation schemas, and set definitions

folium

foolbox

Python toolbox to create adversarial examples that fool neural networks in PyTorch, TensorFlow, Keras, …

football_match_prediction

for-john

Code attempting to generalized Hui-Walter estimation.

forecasting

Time Series Forecasting Best Practices & Examples

forecasting-m5competition

Forecasting 28 day sales for 30490 items sold at Walmart using time series methods, LightGBM and simple LSTM.

forecasting_air_pollution

Stacking a machine learning ensemble for multivariate time series forecasting, with the goal of predicting the one-period ahead PM 2.5 air pollution level, as published in Towards Data Science on Medium.com

forecasting_intermittent_time_series

How To Apply Time Embeddings To A Classification and Quantity Forecast In Tensorflow Embedding time along with categorical and continuous features offsets the errors caused by intermittent time series. This event often occurs in manufacturing or retail businesses that distribute through multiple overlapping distributors or stores and regions with a large parts list. The solution in tensorflow is demonstrated for an international specialty fastener manufacturer with over 2000 part items and 200 distributors in 5 regions. The manufacturer needed pricing support at the quote level by part, distributor and region to project whether or not a quote would become an order and the expected quantity to be sold during a successful order so that it could align with their current demand planning methods. The first session half discusses applications and solutions while the session second half explains the feature development and the non-linear tensorflow model in depth. The annotated open source code in a jupyter notebook is provided for reference.

forest-confidence-interval

Confidence intervals for scikit-learn forest algorithms

forex_timeseries_linearregression_modeling

Use historical exchange rate futures and time series modeling to determine predictable behavior. Use Scikit-Learn linear regression modeling to predict Yen futures returns with lagged Yen futures returns and categorical calendar seasonal effects

former

Simple transformer implementation from scratch in pytorch.

foundationsofmathematics

Lecture notes for the foundations of mathematics course taught in winter semester as part of the MSc in Cognitive Systems.

founders_ddapp

deepdive application to extract founder/company relations from text

fourier-transform

matlab Fourier transform

foxhound

Scikit learn inspired library for gpu-accelerated machine learning

fp-growth

Python implementation of the Frequent Pattern Growth algorithm