# What’s a Deep Neural Network? Deep Nets Explained

Deep neural networks provide an abundance of benefits for statisticians, especially in improving the precision of a machine learning model. The deep net element of a ML model the main reason why we have A.I. from creating cat images to creating art. A photograph styled using the Van Gogh-like effect:

Let’s take a take a look at the deep neural network with a focus on their evolution, as well as the advantages and disadvantages.

## How do you define a deep neural neural network?

In its simplest form, an artificial neural network that has an amount in complexity generally with at least two layers is considered to be an advanced neural network (DNN) which is also called a deep net in the short. Deep networks handle data in complicated methods by using sophisticated mathematical modeling.

To understand deep neural networks however it is best to think of the process as an evolutionary one. Some things were required to be developed prior to the advent of deep neural networks.

The evolution to Deep Neural Networks (DNN)

In the beginning, machine learning required to be developed. ML is a method that automates (through algorithms) statistical models, such as the linear regression model to improve the accuracy of predictions. A model is a model that is able to make predictions regarding things. These prediction are made by using a certain precision. A machine learning model learns, which is a learning process that takes every one of its mistakes and adjusts the weights within the model to produce the model that makes less errors.

The process of learning that is involved in creating models led to the development of artificial neural networks. They use the hidden layer to serve as a way to store and analyze how important one of the inputs contributes to the output. The hidden layer is a place to store information about the importance of the input as well as making connections between the importance of various combinations of inputs.

One hidden layer is considered an Artificial Neural Network (ANN)

Deep neural nets then make use of their ANN component. They argue, if it is so effective in improving models–since each node within the hidden layer creates connections and grades the importance of the input in determining the output, then why not stack increasing numbers of these on top of each other to benefit further from hidden layers?

Thus, the deep net contains several layers hidden. “Deep” is a reference to a model’s layers having several layers deep.

Two or more hidden layers comprise a Deep Neural Network

## Improved accuracy The black box problem

Deep nets enable a model’s performance to improve its accuracy. They let a model be able to take inputs and output. Utilizing deep nets is as easy as cutting and pasting lines of program code to each layer. It doesn’t matter what ML platform you are using in directing the model utilize 2 or 2,000 nodes within each layer is as easy as typing in the numbers 2 , or.

However, using deep nets can create an issue how do these models arrive at their conclusions? Utilizing these tools that are available, a model’s explanationability decreases significantly.

Deep Net Deep Net allows a model to generate generalizations of its own and save those generalizations into an obscure layer called the black box. It is difficult to study. Even if the contents of the box’s black area are understood however, they do not fit into an understanding framework.

## The issue of explanation

A teacher could be able to state that 10 percent of the grade is participation 20 percent is homework as well as quizzes, 30% of which are quizzes and 40% are tests. The numbers are recognized and can be comprehended to predict the total score. So, a teacher’s rubric can be explained. It must be explained so that students can understand what to do to earn a great mark in the class.

A different example is that a basic machine learning model could use information from a basic high school Physics class and calculate an equation for gravity, or that force that is felt on surface of Earth.

A typical linear regression model can be used to provide an equation that can predict outcomes. In the event that the parameters Ball Weight and Drop Height can be used to determine the length of the fall will be the model could be utilized to construct an equation to predict the outcome each time.

• We can learn about the equation as (x) = 1/2 * g (m) is 1/2* g (m/s^2) * t2 (s^2)
• We discover that the mass of an object is not a factor in determine the outcome.

In the event that it is revealed that the layer has been added however, the model will be able to give the correct answer for a ball that falls. However, the equation that could tell which inputs are responsible for the overall output can’t be established. The explanation for the model is unclear.

The question of explainability is being explored, and improvements are being made. Deep nets are a great asset for a model’s performance however, the downside that they bring is the inability to define precisely how the model comes to the conclusion it gets. (By by the way the secret to longevity can be found at 42.)