Articles related to Machine Learning and Natural Language Processing implementation.
This article examines the parts that make up neural networks and deep neural networks, as well as the fundamental different types of models (e.g. regression), their constituent parts (and how they contribute to model accuracy), and which tasks they are designed to learn.
One of the questions that often comes up is what’s the difference between fastText and Word2Vec? Aren’t they both the same? Yes and no. They are conceptually the same, but there is a minor difference—fastText operates at a character level but Word2Vec operates at a word level. Why this difference? Before we dive into fastText , let’s quickly …
Word2Vec is a widely used word representation technique that uses neural networks under the hood. The resulting word representation or embeddings can be used to infer semantic similarity between words and phrases, expand queries, surface related concepts and more. The sky is the limit when it comes to how you can use these embeddings for …
Word2Vec: A Comparison Between CBOW, SkipGram & SkipGramSI Read More »
Previously, we learned how to use CountVectorizer for text processing. In place of CountVectorizer, you also have the option of using HashingVectorizer. In this tutorial, we will learn how HashingVectorizer differs from CountVectorizer and when to use which. CountVectorizer vs. HashingVectorizer HashingVectorizer and CountVectorizer are meant to do the same thing. Which is to convert …
Scikit-learn’s CountVectorizer is used to transform a corpora of text to a vector of term / token counts. It also provides the capability to preprocess your text data prior to generating the vector representation making it a highly flexible feature representation module for text. In this article, we are going to go in-depth into the …
Text classification is the automatic process of predicting one or more categories given a piece of text. For example, predicting if an email is legit or spammy. Thanks to Gmail’s spam classifier, I don’t see or hear from spammy emails! Other than spam detection, text classifiers can be used to determine sentiment in social media …
Build Your First Text Classifier in Python with Logistic Regression Read More »
What are pre-trained embeddings and why? Pre-trained word embeddings are vector representation of words trained on a large dataset. With pre-trained embeddings, you will essentially be using the weights and vocabulary from the end result of the training process done by….someone else! (It could also be you) One benefit of using pre-trained embeddings is that …
Easily Access Pre-trained Word Embeddings with Gensim Read More »
Scikit-learn’s Tfidftransformer and Tfidfvectorizer aim to do the same thing, which is to convert a collection of raw documents to a matrix of TF-IDF features. The differences between the two modules can be quite confusing and it’s hard to know when to use which. This article shows you how to correctly use each module, the …
Learn what text preprocessing is, the different techniques for text preprocessing and a way to estimate how much preprocessing you may need. For those interested, I’ve also made some text preprocessing code snippets in python for you to try. Now, let’s get started!
Inverse Document Frequency (IDF) is a weight indicating how commonly a word is used. The more frequent its usage across documents, the lower its score. The lower the score, the less important the word becomes. For example, the word the appears in almost all English texts and would thus have a very low IDF score …