Images

Fill holes in binary 2D & 3D images fast

Fill Voids Fill holes in binary 2D & 3D images fast. # PYTHON import fill_voids img = … # 2d or 3d binary image filled_image = fill_voids.fill(img, in_place=False) # in_place allows editing of original image filled_image, N = fill_voids.fill(img, return_fill_count=True) # returns number of voxels filled in // C++ #include “fill_voids.hpp” size_t sx, sy, sz; sx = sy = sz = 512; uint8_t* labels = …; // 512x512x512 binary image // modifies labels as a side effect, returns number of […]

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Python

Use FastAPI to Build Web APIs

Creating APIs, or application programming interfaces, is an important part of making your software accessible to a broad range of users. In this tutorial, you will learn the main concepts of FastAPI and how to use it to quickly create web APIs that implement best practices by default. By the end of it, you will be able to start creating production-ready web APIs, and you will have the understanding needed to go deeper and learn more for your specific use […]

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Advanced, Libraries, NLP, Project, Python, Structured Data, Text

FuzzyWuzzy Python Library: Interesting Tool for NLP and Text Analytics

This article was published as a part of the Data Science Blogathon Introduction There are many ways to compare text in python. But, often we search for an easy way to compare text. Comparing text is needed for various text analytics and Natural Language Processing purposes. One of the easiest ways of comparing text in python is using the fuzzy-wuzzy library. Here, we get a score out of 100, based on the similarity of the strings. Basically, we are given the similarity […]

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Beginner, Data Science, Libraries, NLP, Python, Text

Text Analysis with Spacy to Master NLP techniques

This article was published as a part of the Data Science Blogathon Natural Language Processing(NLP) is a branch of Artificial Intelligence that deals with Daily Language. Have you ever wonder how Alexa, Siri, Google Assistant understand us with voice and respond to us. Human Language is the fuzziest and complex. As they receive text input first preprocessing of text happens and many techniques are embedded which lets them understand grammar. In this tutorial, we will study some techniques which are helpful […]

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Data Science, Intermediate, NLP, Python, Text, Unstructured Data

Part 7: Step by Step Guide to Master NLP – Word Embedding in Detail

This article was published as a part of the Data Science Blogathon Introduction This article is part of an ongoing blog series on Natural Language Processing (NLP). In the previous articles (part-5 and 6), we completed the different text vectorization and word embeddings techniques in detail. In this article, firstly we will discuss the co-occurrence matrix, which is also a word vectorization technique and after that, we will be discussing new concepts related to the Word embedding that includes, Applications of […]

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Advanced, Algorithm, NLP, Text

Word Sense Disambiguation: Importance in Natural Language Processing

This article was published as a part of the Data Science Blogathon Introduction In human language, often a word is used in more than one way. Understanding the various usage patterns in the language is important for various Natural Language Processing Applications. ( Image: https://www.pexels.com/photo/book-eyeglasses-eyewear-page-261857/ ) In various usage situations, the same word can mean differently. As, a vast majority of the information online, is in English, for the sake of simplicity, let us deal with examples in the English language only. […]

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Data Science, Intermediate, Libraries, NLP, Python, Text, Word Embeddings

Practical Guide to Word Embedding System

This article was published as a part of the Data Science Blogathon Pre-requisites – Basic knowledge of Python – Understanding of basics of NLP(Natural Language Processing)   Introduction In natural language processing, word embedding is used for the representation of words for Text Analysis, in the form of a vector that performs the encoding of the meaning of the word such that the words which are closer in that vector space are expected to have similar in mean. Consider, boy-men vs […]

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Advanced, Github, NLP, Project, Python, Structured Data, Text

Part 3: Topic Modeling and Latent Dirichlet Allocation (LDA) using Gensim and Sklearn

This article was published as a part of the Data Science Blogathon Overview In the previous two installments, we had understood in detail the common text terms in Natural Language Processing (NLP), what are topics, what is topic modeling, why it is required, its uses, types of models and dwelled deep into one of the important techniques called Latent Dirichlet Allocation (LDA). In this last leg of the Topic Modeling and LDA series, we shall see how to extract topics through […]

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Beginner, Data Science, Machine Learning, NLP, Unstructured Data

Understanding Natural Language Processing -A Beginner’s Guide

This article was published as a part of the Data Science Blogathon Introduction:   Source: https://www.asksid.ai/blog/what-is-natural-language-processing/ Language is very important when we want to communicate with each other. Every human can talk and tell others what they need and listen with language. These languages can be anything like English, Spanish, Hindi, Malayalam, etc… We can express our ideas to others in this medium. Language is one of the critical components of human intelligence. Every day we interact with humans, but how about […]

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Numpy

Creating a Linear Program Solver by Implementing the Simplex Method in Python

Simplex-Numpy Creating a Linear Program Solver by Implementing the Simplex Method in Python with NumPy The example in Simplex.py is : [ 0 2 1 ] A = [ 0 1 1 ] b = [ 20 18 8 ] c = [ 1 -7 -4 ] [ 0 1 0 ] And I already test some random inputs with the use of random function in Numpy: A = np.random.randint(-25, 25,size = (SIZE_MATRIX_X, SIZE_MATRIX_Y)) b = np.random.randint(-25, 25,size = SIZE_MATRIX_X) […]

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