This is the library for the Unbounded Interleaved-State Recurrent Neural Network (UIS-RNN) algorithm, corresponding to the paper Fully Supervised Speaker Diarization.
Extend NaiveForecaster to include all common naive forecasting strategies. For an overview, see this chapter.
introduce seasonal as boolean kwarg, refactor "seasonal_last" and implement "seasonal_mean", so that we can set seasonal=True and strategy="mean" for example
Congratulation to DeepMind! This is a reengineering implementation (on behalf of many other git repo in /support/) of DeepMind's Oct19th publication: [Mastering the Game of Go without Human Knowledge]. The supervised learning approach is more practical for individuals. (This repository has single purpose of education only)
fastText supervised model does not take into account of the document and words representation, it just learns bag of words and labels.
embeddings are computed only on the relation word->label. it would be interesting to learn jointly the semantic relation label<->document<->word<->context.
for now it is only possible to pre-train word embeddings and then use them as initial vectors for the clas
Just under figure: Figure 19.8: Original digit sampled from the MNIST test set (left), corrupted data with on/off imputation (middle), and corrupted data with Gaussian imputation (right).
On line #4: "have been corrupted with Gaussian noise (inputs_currupted_gaussian) and supply the original input"
This is a tensorflow and keras based implementation of SSRNs in the IEEE T-GRS paper "Spectral-Spatial Residual Network for Hyperspectral Image Classification: A 3-D Deep Learning Framework".
https://github.com/rasbt/mlxtend/blob/115278bac14d7fc278885c0722da03f1c3b91604/mlxtend/frequent_patterns/apriori.py#L224