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One of the BEST #MachineLearning Glossary by Google


It will definitely come in handy - https://lnkd.in/gNiE9JT


#machinelearing #glossaries #patternrecognition #artificialintellegence

✴️ @AI_Python_EN
Applied Machine Learning Problem Solving Framework


#machinelearing #datascience

✴️ @AI_Python_EN
**** Advanced NLP with spaCy ****

Credits - Ines Montani

Link - https://course.spacy.io/

#nlp #spacy #naturallanguageprocessing #machinelearing
#datascience

✴️ @AI_Python_EN
Data in the Life: Authorship Attribution in Lennon-McCartney Songs", was just published in the first issue of the HARVARD DATA SCIENCE REVIEW, the inaugural publication of harvard datascience published by the mit press. Combining features of a premier research journal, a leading educational publication, and a popular magazine, HDSR leverages digital technologies and data visualizations to facilitate author-reader interactions globally. Besides our article, the first issue features articles on topics ranging from machine learning models for predicting drug approvals to artificial intelligence. Read it now:
https://bit.ly/2Kuze2q.
#datascience #bigdata #machinelearing #statistics #AI

✴️ @AI_Python_EN
Model interpretation and feature importance is a key for #datascientists to learn when running #machinelearing models. Here is a snippet from the #Genomics perspective.
a) Feature importance scores highlight parts of the input most predictive for the output. For DNA sequence-based models, these can be visualized as a sequence logo of the input sequence, with letter heights proportional to the feature importance score, which may also be negative (as visualized by letters facing upside down).
b ) Perturbation-based approaches perturb each input feature (left) and record the change in model prediction (centre) in the feature importance matrix (right). For DNA sequences, the perturbations correspond to single base substitutions.
c) Backpropagation- based approaches compute the feature importance scores using gradients or augmented gradients such as DeepLIFT (Deep Learning Important FeaTures)* for the input features with respect to model prediction.
Link to this lovely paper:
https://lnkd.in/dfmvP9c

❇️ @AI_Python_EN