What’s Neural Network?

A neural network is a biologically inspired method for computers to learn through analyzing data.

When does a neural network become a deep neural network?

Here’s a neural network, and it’s not particularly deep!. Although it just has three layers, the term “deep” usually refers to dozens, if not hundreds, of them.

The circles represent “neurons,” which are devices that receive input (usually from several preceding neurons) and output (which then typically goes on to provide input for other neurons).

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Cross Validation in R with Example

What Does Cross-Validation Mean?

Cross-validation is a statistical approach for determining how well the results of a statistical investigation generalize to a different data set.

Cross-validation is commonly employed in situations where the goal is prediction and the accuracy of a predictive model’s performance must be estimated.

We explored different stepwise regressions in a previous article and came up with different models, now let’s see how cross-validation can help us choose the best model.

Which model is the most accurate at forecasting?

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AIC Vs BIC

Always we are curious about how well a model explains the relationship between variables.

We can utilize information criteria to determine how successful our model is at explaining relationships.

AIC and BIC are two popular criteria. We will discuss more on both criteria.

Akaike’s Information Criterion (AIC) was created by Hirotugu Akaike, a well-known Japanese statistician.

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Grouping Data in R- Tidyverse Approach

You’ll learn the fundamentals of grouping and how to utilize it to transform and visualize a dataset in this tutorial.

Think about the flight delays in the airline dataset that we discussed in the previous post. We want to know the flight’s delay time varies depending on the day of the week?

As a general thought, people fly most frequently on Mondays and Fridays. Is it true?. For example. Is this going to affect the duration of the flight delay?.

You could find the answers to these questions by grouping the data by reporting airline. After then, compare the outcomes from each day of the week to one another.

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Descriptive Statistics in R

Descriptive Statistics in R, You’ll learn about descriptive statistics in this tutorial, which is one strategy you might employ in exploratory data analysis.

Before you invest time constructing intricate models, it’s necessary to first study your data when you start analyzing data. Calculating descriptive statistics for your data is an easy approach to do so.

Descriptive statistical analysis aids in describing the fundamental characteristics of a dataset and gives a brief description of the sample and data measurements.

One approach to do this is to use the tidyverse dplyr summarise() function.

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How to do Data Format in R

Data Format in R, You’ll learn about data formats and why reformatting data can help you enhance your data analysis in this tutorial.

Data is typically acquired from a variety of sources and by a variety of persons, and it is kept in a variety of formats.

Data formatting is the process of transforming data into a standardized format that allows you to make meaningful comparisons.

Data formatting is an important aspect of dataset cleansing since it guarantees that data is consistent and easy to understand.

Let’s take an example of data set containing Cities, Bangalore, Bengaluru, Bnglr all are the different expressions be used to symbolize Bangalore City.

In the majority of cases, you’ll want to consider them all as a single unit, or format, to make statistical analysis easier later on.

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R Packages for Data Science

An R package is a collection of code, data, documentation, and tests that is easy to share.

The enormous number of packages available in R is one of the reasons for its popularity.

There’s a good probability that someone else has previously solved a problem identical to yours, and you may take advantage of utilizing those r packages.

The tidyverse library, which is a collection of fundamental R programs for data research, will be used extensively in this post.

The tidyverse library’s core contains packages that you’re likely to utilize in your daily data analysis.

There are four sections to the tidyverse library:

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Data Science Tutorials

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Deep Belief Networks and Autoencoders

Deep Belief Networks (DBN) and Autoencoders, Let’s take a look at DBNs and how they are created on top of RBMs.

If you haven’t read the previous posts yet, you can read them by clicking the below links.

1. Introduction to Machine Learning with TensorFlow »
2. Introduction to Deep Learning »
3. Convolutional Neural Networks »
4. Introduction to Recurrent Neural Networks (RNN) »
5. Restricted Boltzmann Machine (RBM) »

Deep Belief Network (DBN)

A DBN is a network that was created to overcome a problem that existed in standard artificial neural networks.

Backpropagation is a phenomenon that might result in “local minima” or “vanishing gradients.”

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Restricted Boltzmann Machine (RBM)

Restricted Boltzmann Machine is used to detect patterns in data, in an unsupervised way.

If you haven’t read the previous posts yet, you can read them by clicking the below links.

1. Introduction to Machine Learning with TensorFlow »
2. Introduction to Deep Learning »
3. Convolutional Neural Networks »
4. Introduction to Recurrent Neural Networks (RNN) »

Restricted Boltzmann machines (RBMs)

RBMs are self-learning shallow neural networks that learn to reassemble data. They’re significant models because they can extract meaningful features from a given input without having to identify them.

RBMs are useful for unsupervised tasks such as

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