## Recently Published

##### Intro to spinner

Spinner is a torch implementation enabling the use of Graph Nets in R-based machine learning workflows.

##### What’s my potential space in this market?

Estimating your potential market space using GNN (Graph Neural Networks) can be improved by gathering relevant data, choosing the appropriate GNN model, preprocessing and normalizing the data, using visualization tools, and considering additional factors.

##### Intro to proteus

Proteus is a Sequence-to-Sequence Variational Model designed for time-feature analysis, leveraging a wide range of distributions for improved accuracy.

##### Intro to janus

janus is a coarse-to-fine optimization for a recommending system based on embedding neural networks

##### Introduction to codez

Seq2seq Time-Feature Analysis using an Encoder-Decoder to project into latent space and a Forward Network to predict the next sequence, based on Tensorflow.

##### Introduction to segen

segen is a model for sequence generalization using the “network” of similarities among sequences for the extrapolation

##### Intro to naive

Naive is a model to extract the common patterns from sequences and extrapolate time features

##### Introduction to dymo

dymo proposes an implementation of Dynamic Mode Decomposition (SVD-approach) to predict multiple time features

##### Introduction to AUDREX

Dynamic regression for time series using Extreme Gradient Boosting with hyper-parameter tuning via Bayesian Optimization or Random Search.

##### Introduction to Jenga

Automatic fast extrapolation of time features using kNN.

##### Introduction to spooky

Time features extrapolation through spectral analysis and jack-knife resampling.

##### Tetragon: a brief introduction

Model for sequence forecasting based on expansion of distance matrix.

##### Introduction to SNAP

SNAP is a tool to design deep neural network with a single line of code.