- Build quick list of recommendations ~ Lenskit
- Source of curated data:
- ml-insights ~ interpret supervised models
- useful ModelXray methods: explain_prediction_difference , feature_dependence_plots, feature_effect_summary,
- handy sentiment analysis library
VADER ( valence Aware Dictionary and Sentiment Reasoner)
- PyMC3 is a python module for Bayesian statistical modeling and model fitting which focuses on advanced Markov chain Monte Carlo and variational fitting algorithms. Its flexibility and extensibility make it applicable to a large suite of problems.
- curated list of R data-analysis packages ~ http://ropensci.org/tutorials/
- Stan for Stan for statistical modeling, data analysis, and prediction in the social, biological, and physical sciences, engineering, and business.
- Use TensorFlow to easily apply LSTM Cell for many computational layers per time step:
for i in range(20): for d in range(4): # d is depth with tf.devices(“/gpu:%d” % d): input = x[i] if d is 0 lease m[d-1] m[d], c[d] = LSTMCell(input, mprev[d], cprev[d]) mprev[d] = m[d] cprev[d] = c[d]