Standard Error in Statistics – Understanding the concept, formula and how to calculate

Standard error of the mean measures how spread out the means of the sample can be from the actual population mean. Standard error allows you to build a relationship between a sample statistic (computed from a smaller sample of the population) and the population’s actual parameter. Standard Error – A practical guide with examples. Photo …

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Confidence Interval in Statistics – Formula and Full Calculation

Confidence interval is a measure to quantify the uncertainty in an estimated statistic (like the mean) when the true population parameter is unknown. Training Custom Text Classification Model in spaCy. Photo by Jessica Wong. You will know 1. What is Confidence Interval? 2. Two types of Confidence Intervals problems 3. Difference between Population parameter vs …

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T Test (Students T Test) – Understanding the math and how it works

T Test (Students T Test) is a statistical significance test that is used to compare the means of two groups and determine if the difference in means is statistically significant. In this one, you’ll understand when to use the T-Test, the different types of T-Test, math behind it, how to determine which test to choose …

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spacy custom text classification

SpaCy Text Classification – How to Train Text Classification Model in spaCy (Solved Example)?

Text Classification is the process categorizing texts into different groups. SpaCy makes custom text classification structured and convenient through the textcat component. Text classification is often used in situations like segregating movie reviews, hotel reviews, news data, primary topic of the text, classifying customer support emails based on complaint type etc. For many real-life cases, …

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How to use tf.function to speed up Python code in Tensorflow

tf.function is a decorator function provided by Tensorflow 2.0 that converts regular python code to a callable Tensorflow graph function, which is usually more performant and python independent. It is used to create portable Tensorflow models.       Introduction Tensorflow released the second version of the library in September 2019. This version, popularly called …

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Tensorflow

TensorFlow vs PyTorch – A Detailed Comparison

Compare the popular deep learning frameworks: Tensorflow vs Pytorch. We will go into the details behind how TensorFlow 1.x, TensorFlow 2.0 and PyTorch compare against eachother. And how does keras fit in here. Table of Contents: Introduction Tensorflow: 1.x vs 2 Difference between static and dynamic computation graph Keras integration or rather centralization What is …

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101 NLP Exercises (using modern libraries)

I hope you found this useful. For more such posts, stay tuned to our page ! Desired Output : [{‘label’: ‘POSITIVE’, ‘score’: 0.9998570084571838}] [{‘label’: ‘NEGATIVE’, ‘score’: 0.9994378089904785}] I hope you found this useful. For more such posts, stay tuned to our page ! 59. How to classify a text as positive or negative sentiment with …

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Training Custom NER models in SpaCy to auto-detect named entities [Complete Guide]

Named-entity recognition (NER) is the process of automatically identifying the entities discussed in a text and classifying them into pre-defined categories. Categories could be entities like ‘person’, ‘organization’, ‘location’ and so on. The spaCy library allows you to train NER models by both updating an existing spacy model to suit the specific context of your …

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Julian Programming Language

For-Loop in Julia

For-loop is a type of loop, that iterates over an iterable object or simply a range of values. It executes some user-defined logic in each iteration. Content Introduction to For-loop in Julia Nested Loop List comprehension in Julia Break Statement in For-loop Continue Statement in For-loop Practice Exercise 1. Introduction to For-loop in Julia For-loop …

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Julian Programming Language

DataFrames in Julia

DataFrame is a 2 dimensional mutable data structure, that is used for handling tabular data. Unlike Arrays and Matrices, a DataFrame can hold columns of different data types The DataFrames package in Julia provides the DataFrame object which is used to hold and manipulate tabular data in a flexible and convenient way. It is quite …

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K-Means Clustering Algorithm from Scratch

K-Means Clustering is an unsupervised learning algorithm that aims to group the observations in a given dataset into clusters. The number of clusters is provided as an input. It forms the clusters by minimizing the sum of the distance of points from their respective cluster centroids. Contents Basic Overview Introduction to K-Means Clustering Steps Involved …

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While-loop in Julia

While-loop is a control flow statement, used to execute a set of statements as long as this given control condition holds true. It falls under the indefinite iteration category. Content Intorduction to While-loop in Julia Break Statement Continue Statement Practice Exercise 1. Introduction to While-loop in Julia While-loop is a control flow statement, used to …

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Function in Julia

Function is a block of organized, reusable code that accepts input and returns output. All the computations you wish to do needs to be declared inside the body of the function. In order to call a function, you need to add ( ) along with any parameters inside it. Content Functions in Julia Return keyword …

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Machine Learning A-Z™: Hands-On Python & R In Data Science

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Machine Learning A-Z™: Hands-On Python & R In Data Science

Machine Learning A-Z™: Hands-On Python & R In Data Science

Machine Learning A-Z™: Hands-On Python & R In Data Science

Machine Learning A-Z™: Hands-On Python & R In Data Science

Machine Learning A-Z™: Hands-On Python & R In Data Science