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Articles related to "model"


My NLP learning journey

  • But a computer needs specialized processing techniques to understand raw text data.
  • That’s why NLP attempts to use a variety of techniques to create structure out of text data.
  • I will introduce a little bit nltk and spacy, both state-of-the-art libraries in NLP and the difference between them.
  • Spacy: is an open-source Python library that parses and “understands” large volumes of text.
  • The first step in processing text is to split up all the parts (words & punctuation) into “tokens”.
  • And that’s exactly what Spacy is designed to do: you put in raw text and get back a Doc object, that comes with a variety of annotations.
  • Given enough data, usage, and contexts, Word2vec can make highly accurate guesses about a word’s meaning based on past appearances.
  • LDA was introduced back in 2003 to tackle the problem of modeling text corpora and collections of discrete data.

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My NLP learning journey

  • But a computer needs specialized processing techniques to understand raw text data.
  • That’s why NLP attempts to use a variety of techniques to create structure out of text data.
  • I will introduce a little bit nltk and spacy, both state-of-the-art libraries in NLP and the difference between them.
  • Spacy: is an open-source Python library that parses and “understands” large volumes of text.
  • The first step in processing text is to split up all the parts (words & punctuation) into “tokens”.
  • And that’s exactly what Spacy is designed to do: you put in raw text and get back a Doc object, that comes with a variety of annotations.
  • Given enough data, usage, and contexts, Word2vec can make highly accurate guesses about a word’s meaning based on past appearances.
  • LDA was introduced back in 2003 to tackle the problem of modeling text corpora and collections of discrete data.

save | comments | report | share on


My NLP learning journey

  • But a computer needs specialized processing techniques to understand raw text data.
  • That’s why NLP attempts to use a variety of techniques to create structure out of text data.
  • I will introduce a little bit nltk and spacy, both state-of-the-art libraries in NLP and the difference between them.
  • Spacy: is an open-source Python library that parses and “understands” large volumes of text.
  • The first step in processing text is to split up all the parts (words & punctuation) into “tokens”.
  • And that’s exactly what Spacy is designed to do: you put in raw text and get back a Doc object, that comes with a variety of annotations.
  • Given enough data, usage, and contexts, Word2vec can make highly accurate guesses about a word’s meaning based on past appearances.
  • LDA was introduced back in 2003 to tackle the problem of modeling text corpora and collections of discrete data.

save | comments | report | share on


How analytics maturity models are stunting data science teams

  • A strong reason why teams get bogged down at the lower end of the maturity model is that management paradigms that make descriptive and diagnostic analytics effective may be a death knell for predictive and prescriptive work.
  • As an example, if I am building a machine learning model for predictive maintenance, and find that the available data carries no useful signals, failing after two weeks of experimentation on a laptop is much better than failing with a six month budgeted project and a team of ten.
  • To recap: a primary way maturity models damage teams is when companies take the methods of management that worked for delivering descriptive analytics solutions, and impose them on advanced analytics work without modifying the approach to account for data uncertainty.
  • It requires mature processes that acknowledge data uncertainty, safe spaces to experiment to de-risk advanced analytics work, proper model operations post go-live and financial models that are tailored for products instead of projects.

save | comments | report | share on


My NLP learning journey

  • But a computer needs specialized processing techniques to understand raw text data.
  • That’s why NLP attempts to use a variety of techniques to create structure out of text data.
  • I will introduce a little bit nltk and spacy, both state-of-the-art libraries in NLP and the difference between them.
  • Spacy: is an open-source Python library that parses and “understands” large volumes of text.
  • The first step in processing text is to split up all the parts (words & punctuation) into “tokens”.
  • And that’s exactly what Spacy is designed to do: you put in raw text and get back a Doc object, that comes with a variety of annotations.
  • Given enough data, usage, and contexts, Word2vec can make highly accurate guesses about a word’s meaning based on past appearances.
  • LDA was introduced back in 2003 to tackle the problem of modeling text corpora and collections of discrete data.

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Political Data Science: A tale of tweets

  • The current political situation in Scotland after the Brexit vote, and most recently, Boris Johnson’s win in the winter General Election of 2019, is very heated.
  • I then performed a systematic comparison with a Deep Learning Recurrent Neural Network (RNN) known as Long-Short-Term-Memory (LSTM) Network.
  • With Grid Search you set up a grid of hyperparameter values and for each combination, train a model and score on the validation data.
  • I passed the combined hyperparameters to the GridsearchCV object for each classifier and 10 folds for the cross validation which means that for every parameter combination, the grid ran 10 different iterations with a different test set every time (this took a while…).
  • And if we want a neural network to understand our tweets, we need one that can learn from what it reads and build on it.

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The Non-Treachery of Dataset

  • There were various efforts for letting AI create art (e.g., a Model, trained on 24k Painting Dataset from Kaggle).
  • On one hand, we would like to train an end-to-end deep convolution model to investigate the capability of the deep model in fine-art painting classification problem.
  • On the other hand, we argue that classification of fine-art collections is a more challenging problem in comparison to objects or face recognition.
  • So does AI has the imagination to recognize art, a non-representational production of the human brain, heart, and hand?
  • AI is perplexed as well — the most it can do, is distinguishing between artist styles, depicted objects, and art movement features.
  • Exactly: ArtGAN cannot reconstruct famous paintings (in this case because it’s trained on the huge dataset from various art epochs).
  • It’s a disruptive force which lets us re-consider the human factor in the concept of art.

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On Implementing Deep Learning Library from Scratch in Python

  • They provide the necessary recipe to update model parameters using their gradients with respect to the optimization objective.
  • On the back-end side, these libraries provide support for automatically calculating gradients of the loss function with respect to various parameters in the model.
  • The backward(…) method receives partial derivatives of the loss function with respect to the operator’s output and implements the partial derivatives of loss with respect to the operator’s input and parameters (if there are any).
  • The backward(…) function receives partial derivatives dY of loss with respect to the output Y and implements the partial derivatives with respect to input X and parameters W and b.
  • This method updates the model parameters using their partial derivatives with respect to the loss we are optimizing.
  • Inspired by the blog-post of Andrej Karapathy, I am going to train a hidden layer neural network model on spiral data.

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How analytics maturity models are stunting data science teams

  • A strong reason why teams get bogged down at the lower end of the maturity model is that management paradigms that make descriptive and diagnostic analytics effective may be a death knell for predictive and prescriptive work.
  • As an example, if I am building a machine learning model for predictive maintenance, and find that the available data carries no useful signals, failing after two weeks of experimentation on a laptop is much better than failing with a six month budgeted project and a team of ten.
  • To recap: a primary way maturity models damage teams is when companies take the methods of management that worked for delivering descriptive analytics solutions, and impose them on advanced analytics work without modifying the approach to account for data uncertainty.
  • It requires mature processes that acknowledge data uncertainty, safe spaces to experiment to de-risk advanced analytics work, proper model operations post go-live and financial models that are tailored for products instead of projects.

save | comments | report | share on


My NLP learning journey

  • But a computer needs specialized processing techniques to understand raw text data.
  • That’s why NLP attempts to use a variety of techniques to create structure out of text data.
  • I will introduce a little bit nltk and spacy, both state-of-the-art libraries in NLP and the difference between them.
  • Spacy: is an open-source Python library that parses and “understands” large volumes of text.
  • The first step in processing text is to split up all the parts (words & punctuation) into “tokens”.
  • And that’s exactly what Spacy is designed to do: you put in raw text and get back a Doc object, that comes with a variety of annotations.
  • Given enough data, usage, and contexts, Word2vec can make highly accurate guesses about a word’s meaning based on past appearances.
  • LDA was introduced back in 2003 to tackle the problem of modeling text corpora and collections of discrete data.

save | comments | report | share on