Backpropagation in Neural Networks | Back Propagation Algorithm with Examples | Simplilearn

Summary

This video script delves into the importance and workings of back propagation in neural networks. It explains how back propagation is essential for reducing errors and improving accuracy in machine learning models. The script covers the concept, process, benefits, and applications of back propagation, illustrating its role in training neural networks for tasks like speech recognition. It emphasizes back propagation's efficiency and versatility, highlighting its widespread use in various fields.

Takeaways

• 🧠 Backpropagation is essential for neural networks as it allows for the correction of errors by traveling information backward from output to input nodes.
• 📈 It is crucial for improving accuracy in data mining and machine learning tasks by adjusting the weights of the network through gradient descent.
• 🌟 The concept of backpropagation in neural networks was first introduced in the 1960s, inspired by biological neural networks.
• 🔄 The process involves forward propagation where data moves through the network until it reaches the output layer, followed by backpropagation to adjust weights based on error.
• 📊 Backpropagation calculates the gradient of the loss function, which is the derivative of the loss with respect to the weights.
• 🔍 It enables the neural network to understand which outputs need to be increased and which need to be decreased to minimize the loss.
• 🛠️ Backpropagation is quick, easy, and simple to implement, making it a versatile method for neural network training.
• 🔧 It does not require prior knowledge of the network structure, only input numbers are needed to tune the parameters.
• 📚 The method is generally effective without needing to consider specific characteristics of the function being learned.
• 🗣️ Applications of backpropagation are widespread, including fields like speech recognition and signature recognition.
• 🌐 Backpropagation is fundamental in almost every area where neural networks are utilized, highlighting its importance in the field of AI.

Q & A

What is the purpose of backpropagation in neural networks?

Backpropagation is used to reduce the error by calculating the gradient of the loss function and adjusting the weights of the network to improve accuracy in data mining and machine learning tasks.

Why is backpropagation considered the building block of a neural network?

Backpropagation is fundamental to neural networks because it enables the training process by allowing the network to learn from its mistakes and adjust its parameters accordingly.

What is the initial step in the backpropagation algorithm?

The initial step is forward propagation, where data passes through the neural network from the input layer to the output layer, generating an output based on the current weights.

How does the backpropagation algorithm work in terms of error calculation?

Backpropagation works by calculating the gradient of the loss function by taking the derivative with respect to the weights, which helps in understanding how much each weight contributes to the error.

What is the role of gradient descent in the context of backpropagation?

Gradient descent uses the gradients calculated by backpropagation to update the weights of the network, aiming to minimize the loss function by iteratively adjusting the weights in the direction that reduces the error.

Why is backpropagation necessary even if there is a forward propagation method?

Backpropagation is necessary because forward propagation only allows the network to make predictions, but backpropagation enables the network to learn from these predictions by adjusting the weights based on the error.

What is the difference between forward propagation and backpropagation?

Forward propagation is the process of passing data through the network to make predictions, while backpropagation is the process of adjusting the network's weights based on the error from the predictions.

What are some of the benefits of using backpropagation?

Benefits include its quick, easy, and simple implementation, versatility as it doesn't require prior network knowledge, and its effectiveness in a wide range of applications.

In which fields can backpropagation be applied?

Backpropagation can be applied in various fields such as speech recognition, voice and signature recognition, and any other area where neural networks are utilized.

What is the significance of the activation function in neural networks?

The activation function determines the output of a neuron given an input by adding a non-linearity to the model, which is crucial for the network's ability to learn complex patterns.

Why is the output layer's role important in backpropagation?

The output layer's role is important because it generates the final prediction, and the error is calculated based on the difference between this prediction and the actual target, which initiates the backpropagation of errors.

Outlines

00:00

🧠 Introduction to Backpropagation

This paragraph introduces the concept of backpropagation as a fundamental aspect of neural networks. It emphasizes the importance of understanding backpropagation for improving accuracy in data mining and machine learning. The speaker invites viewers to subscribe to the Simply Learns YouTube channel and engage with the content by commenting on a question about neural network components. The agenda for the video includes an explanation of backpropagation, its role in neural networks, how it operates, its benefits, and its applications.

05:01

🔍 Understanding Backpropagation in Neural Networks

This paragraph delves into the specifics of backpropagation in neural networks, tracing its origins to the 1960s. It describes an artificial neural network as a system of interconnected input and output units, each with a weight determined by a software program. The paragraph explains how backpropagation is utilized to minimize errors by adjusting weights through the gradient of the loss function. It details the process of forward propagation, where data moves from the input layer through hidden layers to the output layer, and how backpropagation works in reverse to update weights and reduce loss, ultimately improving the network's performance across various applications.

Keywords

💡Back Propagation

Back propagation is a fundamental algorithm in neural networks that is used to minimize errors by adjusting the network's weights. It is essential for training neural networks as it allows the network to learn from its mistakes. In the script, back propagation is described as the process where errors 'travel back from input nodes to output nodes' and is applied to improve accuracy in data mining and machine learning.

💡Neural Network

A neural network is a series of algorithms designed to recognize patterns. It is modeled loosely after the human brain, which is composed of neurons. In the video script, neural networks are described as being made up of connected input and output units, each with a certain weight, and are based on biological neural networks. They are used in various fields such as speech recognition and are trained using back propagation.

💡Forward Propagation

Forward propagation is the process in which input data is passed through a neural network until it reaches the output layer. It is the initial step in neural network processing where data flows from the input layer, through hidden layers (if any), to the output layer. In the script, forward propagation is mentioned in the context of how data passes through the neural network until it reaches the output layer.

💡Cost Function

The cost function is a mathematical equation that measures how well the neural network is performing. It calculates the difference between the predicted output and the actual output. In the script, back propagation is applied to reduce the cost function, which is a measure of the error in the network's predictions.

💡Gradient Descent

Gradient descent is an optimization algorithm used to minimize the cost function in neural networks. It adjusts the weights of the network in the direction that minimally decreases the cost function. In the script, gradient descent is mentioned in relation to back propagation, where it uses the output layer to understand how to adjust the weights to reduce the loss.

💡Activation Function

An activation function in a neural network determines the output of a node given an input or set of inputs by adding a non-linear element to the model. It is used to introduce non-linearity into the network, which allows it to learn more complex patterns. In the script, the highest activation function is considered the suitable output match for the corresponding input.

💡Input Layer

The input layer is the first layer in a neural network that receives the initial data for processing. It is where the raw data enters the network. The script mentions the input layer in the context of how data passes through the neural network until it reaches the output layer.

💡Output Layer

The output layer is the final layer in a neural network that produces the result of the computation. It is where the network's predictions are made. In the script, the output layer is discussed in terms of how it receives data from the input layer and how back propagation is used to adjust its predictions.

💡Hidden Layer

Hidden layers are the layers between the input and output layers in a neural network. They perform most of the computations needed to learn from the input data. The script mentions a sample network with two hidden layers, indicating that these layers play a crucial role in the network's learning process.

💡Loss Function

The loss function is a function that calculates the difference between the predicted output and the actual output. It is used to measure the error of the model during training. In the script, the loss function is mentioned in the context of back propagation, where it is used to calculate the gradient that helps in adjusting the network's weights.

💡Weight

In a neural network, weights are the parameters that are adjusted during training to minimize the loss function. They determine the importance of each input feature in the output. The script discusses how weights are multiplied with the output of the previous layer to produce the activation function, and how back propagation is used to adjust these weights.

Highlights

Back propagation is essential for understanding the importance and application of this method in neural networks.

Back propagation can be considered the building block of a neural network.

The concept of back propagation in neural networks was first introduced in the 1960s.

An artificial neural network is composed of interconnected input and output units with associated weights.

Back propagation is used to reduce the cost function and errors in a neural network.

Forward propagation is the process where data passes through the neural network until it reaches the output layer.

Back propagation calculates the gradient of the loss function using the derivative with respect to the weights.

Gradient descent is used in conjunction with back propagation to adjust weights and reduce loss.

Back propagation allows for the increase of the correct output node's value and decrease of the incorrect ones, thus minimizing loss.

Back propagation is quick, easy, and simple to implement, making it versatile for various applications.

It does not require prior network knowledge and can be tuned with only input numbers as parameters.

Back propagation is a common method that works effectively without needing to consider the characteristics of the function being taught.

Applications of back propagation include speech recognition and voice and signature identification.

Neural networks trained with back propagation can pronounce each letter in a word and a sentence.

Back propagation is fundamental in almost every field where neural networks are utilized.

The video encourages viewers to subscribe to the Simply Learn YouTube channel for similar educational content.

The video concludes with an invitation to subscribe and watch more videos to enhance knowledge and get certified.

Transcripts

foreign

you might have thought though we have a

forward propagation method why is there

need for back propagation why is it

important to learn back propagation well

I guarantee you that after watching this

video you will understand why back

propagation is needed and why it is

applied everywhere hey everyone I hope

you all are doing well and in this video

we will be looking in detail about back

propagation back propagation can be

called the building block of a neural

network and you'll understand why after

watching the complete video but before

we get started consider subscribing to

Simply learns YouTube channel and hit

that Bell icon and that way you'll be

the first to get notified when we post

similar content

now let's move forward and look at what

the agenda is for today

first we'll look at what is back

propagation and after that what is back

propagation in a neural network next how

does back propagation in a neural

network work

and further we will understand benefits

of back propagation and finally

applications

but before moving forward let me ask you

a question which among the following is

not a neural network input layer output

layer propagation layer hidden layer

please leave your answer in the comments

section below and stay tuned to find the

answer

so what is a back propagation algorithm

back propagation is an algorithm which

is created to test errors which will

travel back from input nodes to Output

nodes it is applied to improve accuracy

in Data Mining and machine learning

coming to back propagation in neural

networks what is back propagation in

neural networks well the concept of back

propagation in neural networks was first

introduced in the 1960s an artificial

neural network is made up of Bunches of

connected input and output units Each of

which is connected by a software program

and has a certain weight

this kind of network is based on

biological neural networks which contain

neurons coupled to one another across

different network levels in this

instance neurons are shown as nodes well

now that we've understood what the back

propagation algorithm is we'll come to

the next topic the working of the back

propagation algorithm

so the back propagation algorithm is

applied to reduce cost function and to

reduce errors we have a sample network

with two hidden layers and a single

input layer where data passes in and

this data is finally received by the

output layer through all these neural

networks whenever we pass the data to

the input layer it will pass through the

neural network until it reaches the

output layer each model receives its

input from the previous layer and the

previous layer output is Multiplied with

weight which will give the activation

function or a and the result of this is

passed as an input for the next layer

and this process continues to happen

until we reach the output layer of each

neural network and this process is

referred to as forward propagation so

after reaching the output layer we get

the resulting output for the given model

from the input

output with the highest activation will

be considered as the suitable output

match for the corresponding input

loss is calculated on what the model has

predicted as input and what the actual

input is now here back propagation is

used to calculate the gradient of the

loss function in back propagation we are

coming back from the network and now we

have the output generated by the given

input now gradient descent looks at the

output layer and gradient descent

understands that the value of one output

increases and the other will decrease

we know that the output is derived from

the previous layer's output multiplied

by the weight of the network

back propagation is the tool that the

neural network considers in order to

calculate the gradient of the loss

function it is calculated by taking the

derivative of loss function by weight

and now we have the loss function

calculated from the previous layer now

gradient descent will start calculating

the value through the previous network

using back propagation with the aim of

reducing loss

we know that the value is coming from

the weighted sum of the previous Network

being multiplied by the output of the

previous layer and this process is

repeated until we update the value of

the previous sum

so as we can see we are going backwards

and from this we can increase the value

of the correct output node and decrease

the value of the incorrect input node

thus it will reduce the loss and the

activation function which has the

highest value should increase and the

lower value will decrease

do you remember the question that I

asked earlier and which among the

following is not a neural network well I

hope you got the answer by now the

answer for the question is propagation

layer

and to do this we need to change the

output of the previous layer we cannot

directly change it so we're going to

change its previous Network and why do

we need to choose back propagation well

below are some of the important factors

on why we need to choose back

propagation

so back propagation is quick easy and

simple to implement it is a versatile

method because it doesn't need prior

Network knowledge and only has input

numbers as parameters to tune

and there is no need to make any special

note of the characteristics of the

function that must be taught because it

is a common way that typically Works

effectively

and the final topic is applications of

back propagation

it can be used in the field of speech

recognition and it can also be used to

recognize voice and signature

the neural network has received training

to pronounce each letter in a word and a

sentence and the neural network has

received training therefore back

propagation finds itself in almost every

field where neural networks are used now

in case of any questions please mention

them in the comments section below and I

hope you enjoyed watching this video

Happy learning

[Music]

hi there if you like this video

subscribe to the simply learn YouTube

channel and click here to watch similar

videos to nerd up and get certified

click here