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Data Science Articles
Page 12 of 13
What are Autoencoders in Deep Learning?
Introduction Data encodings are unsupervised learned using an artificial neural network called an autoencoder. An autoencoder learns a lower-dimensional form (encoding) for a higher-dimensional data to learn a higher-dimensional data in a lower-dimensional form, frequently for dimensionality reduction. Autoencoders Autoencoders are very useful in the field of unsupervised machine learning. They can be used to reduce the data's size and compress it. Principle Component Analysis (PCA), which finds the directions along which data can be extrapolated with the least amount of variance, and autoencoders, which reconstruct our original input from a compressed version of it, differ from one another. If ...
Read MoreWhat Are Self Organizing Maps - Kohonen Map?
Introduction Kohonen proposed the idea of a self-organizing map (SOM) in the first place. Since it is an unsupervised neural network that is trained using unsupervised learning methods to create a low-dimensional, discretized representation from the input space of the training samples, it is a way to minimise the dimensions of the data. A map is a common name for this representation. This article will walk through a Kohonen Map beginner's guide, which is a well-known self-organizing map. To begin, let's define what self-organizing maps are. Self-Organizing Maps Self-organizing maps, also known as Kohonen maps or SOMs, are ...
Read MoreWhy should you learn machine learning and artificial intelligence
Introduction Due to the rising need for qualified individuals, interesting job prospects, commercial applications, customization, and innovation, studying machine learning (ML) and artificial intelligence (AI) is becoming more and more crucial. Professionals who can design, construct, and maintain these systems are required as more businesses use AI and ML technology. In addition to providing interesting job prospects across a range of industries, ML and AI may assist organizations in streamlining operations, making data-driven choices, and increasing productivity and profitability. Moreover, ML and AI are at the forefront of technological advancement and may be utilized to tailor client experiences. People can ...
Read MoreWhat is Overfitting and how to avoid it?
Introduction In statistics, the phrase "overfitting" is used to describe a modeling error that happens when a function correlates too tightly to a certain set of data. As a result, overfitting could not be able to fit new data, which could reduce the precision of forecasting future observations. Examining validation measures like accuracy and loss might show overfitting. The validation measures frequently increase until a point at which they level out or start to drop when the model is affected by overfitting. During an upward trend, the model looks for a good match, and once it finds one, the movement ...
Read MoreRelationship between AI and Data
Introduction Artificial intelligence (AI) successfully imitates human cognition and reasoning processes for use in everyday applications. This is frequently observed in cybersecurity with work automation and threat variant prediction. But the fuel that is being provided to any AI system, like a car, is what powers it. However, there is a lot more data than fuel. Therefore, the goal of this article is to clarify the crucial role that data plays in AI. Relationship Between AI and Data Below are a few Relationships Between AI and Data It’s Garbage in and Garbages out An AI system's "output, " the ...
Read MoreRole of time series algorithms in Data Science
In order to recognize and predict trends in data gathered over time, time series analysis is a potent technique. Each data point in a time series represents a distinct moment in time and is gathered over time. Stock prices, weather information, and website traffic are a few examples of time series data. In a variety of disciplines, including economics, finance, and weather forecasting, time series data is often employed. The practice of employing statistical methods to comprehend and forecast the data across time is known as time series analysis. Because it enables us to spot patterns, trends, and correlations in ...
Read MoreFunctional Programming with purrr
Functional programming is a programming methodology in which we construct programs by constructing and applying functions. More specifically in programs, we apply sequential pure functions rather than statements. A pure function is a function that accepts an input and produces a consistent value as an output. Also, during this process no augment or input stream is modified. Such functions are capable of doing a single operation but for carrying out complex operations we can combine them into sequences. In this tutorial, we will discuss functional programming using purr. Nowadays, Functional programming is important to master due to its capability to ...
Read MoreDealing with Missing Data in R
In data science, one of the common tasks is dealing with missing data. If we have missing data in your dataset, there are several ways to handle it in R programming. One way is to simply remove any rows or columns that contain missing data. Another way to handle missing data is to impute the missing values using a statistical method. This means replacing the missing values with estimates based on the other values in the dataset. For example, we can replace missing values with the mean or median value of the variable in which the missing values are found. ...
Read MoreWriting Efficient R Code
Writing efficient code is very important as it makes the development time faster and leads our program to be able to understand, debug and maintain easily. We will discuss various techniques like benchmarking, vectorization and parallel programming to make our R code faster. You must learn these techniques if you are aspiring to be a data scientist. So, let’s get started − Benchmarking One of the easiest optimizations is to have the latest R version to work for. The new version cannot modify our existing code but it always comes with robust library functions that provide improved execution time. The ...
Read MoreString Manipulation in R with stringr
The stringr package is a popular R package that provides functions and tools for manipulating and processing strings in R. This package provides a consistent and convenient interface for working with strings, and it offers a wide range of functions for tasks such as searching, matching, replacing, and splitting strings. In this article, we will discuss string manipulation in R with "stringr” package. The “stringr” package provides us the following families of functions in “stringr” − Character manipulating functions: Such functions allows us to deal with the characters of a string. A family of functions to deal with whitespaces. ...
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