Simple explanation of convolutional neural network | Deep Learning Tutorial 23 (Tensorflow & Python)

Summary

This script offers a simplified explanation of Convolutional Neural Networks (CNNs), ideal for beginners. It illustrates how CNNs can recognize patterns like handwritten digits and complex images by using filters to detect features like edges and shapes. The script clarifies the concept of feature maps, the role of ReLU for non-linearity, and the importance of pooling to reduce dimensions and computation. It also touches on the self-learning capability of CNNs to adjust filters during training, making it an intuitive and powerful tool for computer vision tasks.

Takeaways

• 🧠 Convolutional Neural Networks (CNNs) are designed to recognize patterns in images, such as handwritten digits, by using a grid of numerical values representing pixel intensities.
• 🔍 Traditional neural networks struggle with image recognition due to their inability to handle variations in image positioning and the immense computational load for large images.
• 🌟 CNNs utilize filters or kernels to detect specific features within an image, such as edges or shapes, by applying a convolution operation that scans the image in a sliding window fashion.
• 🔑 The convolution operation involves multiplying the filter values with the corresponding image section and summing them up to create a feature map, which highlights areas of the image that match the filter's pattern.
• 👀 Human brains recognize images by detecting features like eyes, nose, and ears, which is similar to how CNNs use different filters to identify features in images.
• 📈 CNNs reduce computational complexity through parameter sharing, where the same filter parameters are applied across the entire image, and through pooling layers that reduce the spatial dimensions of the feature maps.
• 🔄 Pooling layers, such as max pooling, help in making the CNN invariant to small translations and distortions in the image by selecting the most prominent features within a region.
• 🔧 The use of ReLU (Rectified Linear Unit) activation function introduces non-linearity into the CNN, which is essential for solving complex pattern recognition tasks.
• 🤖 CNNs learn the optimal filters during the training phase through backpropagation, without the need for manual filter selection, allowing the network to automatically adapt to the features present in the training data.
• 🔄 Data augmentation techniques, such as rotating or scaling images, can be used to increase the robustness of CNNs to variations like rotation and scaling in the input images.
• 📚 The script is an educational resource provided by Daval Patel, who offers tutorials on data science, machine learning, Python programming, and career guidance on his YouTube channel.

Q & A

What is the main issue with using a grid of numbers to represent an image for a computer?

The main issue is that it is too hard-coded and sensitive to shifts or variations in the image. For example, a slight shift in the position of a handwritten digit can change the representation, causing the computer to fail in recognizing the digit.

Why is a dense neural network not efficient for handling larger images like the one of a koala?

A dense neural network would require an enormous number of weights to be calculated between the input and hidden layers, leading to a high computational cost that is impractical for large images with many pixels and RGB channels.

How do convolutional neural networks (CNNs) address the issue of local features in images?

CNNs use filters or convolution operations to detect local features in images. These filters act as feature detectors that can identify patterns regardless of their position in the image, thus addressing the issue of locality.

What is the purpose of a feature map in CNNs?

A feature map is the result of applying a convolution operation with a filter. It highlights areas in the image where the specific feature the filter is designed to detect is present, effectively capturing the presence of that feature throughout the image.

How does the stride parameter affect the size of the feature map?

The stride determines the step size the filter moves across the image. A larger stride results in a smaller feature map because fewer positions are covered by the filter.

What is pooling, and what are its benefits in CNNs?

Pooling is an operation that reduces the dimensions of a feature map, typically by taking the maximum (max pooling) or average (average pooling) value within a certain window. It benefits CNNs by reducing computational load, mitigating overfitting, and making the model more tolerant to variations and distortions.

How does the ReLU (Rectified Linear Unit) activation function introduce non-linearity into a CNN?

The ReLU activation function introduces non-linearity by setting all negative values in the feature map to zero, while keeping positive values unchanged. This simple operation allows the model to learn complex patterns in the data.

What is the role of the fully connected layer in a CNN after the convolution and pooling layers?

The fully connected layer serves as the classification part of the CNN. It takes the flattened output from the convolution and pooling layers and uses it to make predictions about the image, handling the variety in inputs to classify them effectively.

How does a CNN learn the filters during the training process?

During training, a CNN uses backpropagation to adjust the filters based on the training data. The network starts with random filters and learns the optimal filter values through the training process to effectively detect features in the images.

What is data augmentation, and how does it help in training CNNs to handle variations like rotation and scaling?

Data augmentation is a technique where new training samples are artificially created by applying transformations like rotation, scaling, and translation to the existing data. This helps the CNN to learn to recognize features under various conditions and improves its ability to generalize across different image variations.

Outlines

00:00

🧠 Introduction to Convolutional Neural Networks

This paragraph introduces the concept of Convolutional Neural Networks (CNNs) in a simplified manner, suitable for high school students. It discusses the challenge of recognizing handwritten digits like '9' using a computer, and the limitations of hard-coded grids and RGB values. The paragraph explains how traditional Artificial Neural Networks (ANNs) struggle with large images due to the computational complexity of millions of weights. It also touches upon the human brain's ability to recognize features in images, such as the distinct parts of a koala, and sets the stage for the need of CNNs to mimic this feature detection capability.

05:02

🔍 The Role of Filters in CNNs

This section delves into the function of filters in CNNs, using the example of recognizing the digit '9'. It describes how filters, or kernels, are used to detect specific features in an image, such as the loopy circle pattern at the top, the vertical line in the middle, and the diagonal line at the end. The paragraph explains the convolution operation, which involves applying these filters to the original image to create a feature map that highlights areas where the specific features are detected. It also discusses the importance of stride and the size of the filter, and how the feature map serves as a detector for specific features, making the model location invariant.

10:02

🌐 Advanced CNN Concepts: Feature Maps and Pooling

Building upon the foundation of filters, this paragraph introduces the concept of feature maps and pooling layers in CNNs. It explains how multiple feature maps can be generated by applying different filters to detect various features of an object, such as the eyes, nose, and ears of a koala. The paragraph also describes how pooling operations, specifically max pooling, reduce the dimensionality of the feature maps, thus decreasing computational load and preventing overfitting. It highlights the benefits of pooling, including feature invariance to small variations and distortions in the image.

15:05

🔧 The Mechanics of CNNs: Convolution, Activation, and Pooling

This section provides a deeper understanding of the mechanics within a CNN, emphasizing the iterative process of applying convolution, activation functions like ReLU, and pooling layers. It explains how these components work together to progressively reduce the spatial dimensions of the data while extracting increasingly complex features. The paragraph also touches on the concept of parameter sharing in convolution, which contributes to the efficiency of CNNs, and the role of fully connected layers in classification after feature extraction.

20:05

🛠 Handling Complex Variations in CNNs

This paragraph addresses the limitations of CNNs in handling variations such as rotation and scale, and introduces the concept of data augmentation as a solution. It explains how training a CNN with a diverse set of samples, including rotated and scaled images, can help the network learn to recognize features despite these variations. The paragraph also summarizes the key components and benefits of CNNs, such as connection sparsity, location invariant feature detection, and parameter sharing, and hints at the self-learning capability of CNNs during training.

📚 Summary and Future Outlook of CNNs

The final paragraph provides a concise summary of the entire explanation of CNNs, outlining the steps involved in processing an image with a CNN, from convolution and activation to pooling and classification. It emphasizes the network's ability to learn filters automatically during training, a process facilitated by backpropagation. The paragraph also introduces the author, Daval Patel, and invites viewers to follow his YouTube channel for further tutorials on data science, machine learning, and deep learning, including practical coding applications of CNNs.

Keywords

💡Convolutional Neural Network (CNN)

A Convolutional Neural Network is a type of deep learning algorithm widely used in computer vision tasks. It is designed to process data with a grid-like topology, such as images. In the video, CNNs are explained as a way to handle the variability in image recognition by using filters to detect features like edges and shapes, which is crucial for tasks like recognizing handwritten digits or identifying objects in photos.

💡Feature Map

A feature map is the result of applying a convolution operation to an input image with a specific filter. It highlights regions in the image where the filter's pattern is detected. In the context of the video, feature maps are used to detect and visualize features like the round eyes or fluffy ears of a koala, playing a key role in object recognition within a CNN.

💡Filter or Kernel

In the video, a filter or kernel is a small matrix used in convolution operations to scan the input image and detect specific features. The script explains that these filters can be learned by the CNN during training to automatically identify patterns like the loopy circle pattern of the digit '9' or the eyes of a koala.

💡Stride

Stride refers to the step size the filter moves across the image during the convolution process. The script uses the term to explain how the feature map's resolution can be controlled—smaller strides result in a more detailed feature map, while larger strides can reduce the spatial dimensions of the output.

💡ReLU (Rectified Linear Unit)

ReLU is an activation function used in neural networks, including CNNs, to introduce non-linearity into the model. The video script describes ReLU as a function that replaces all negative values in a feature map with zero, simplifying the model and making it faster to train without losing the essential information.

💡Pooling

Pooling is an operation used in CNNs to reduce the spatial size of the feature maps, which in turn reduces the number of parameters and computation in the network. The video mentions max pooling, where the maximum value in a region is taken, as a way to make the model invariant to small translations and distortions in the input image.

💡Fully Connected Layer

A fully connected layer, as mentioned in the script, is a traditional neural network layer where each neuron is connected to every neuron in the next layer. In the context of CNNs, these layers are typically placed after the convolution and pooling layers to perform classification based on the features extracted by the CNN.

💡Backpropagation

Backpropagation is the algorithm used to train neural networks, including CNNs, by adjusting the weights of the network to minimize the error in predictions. The video script highlights that during training, CNNs use backpropagation to learn the optimal filters for feature detection automatically.

💡Parameter Sharing

Parameter sharing is a concept where the same filter parameters are applied across the entire input image in a CNN. The script explains this as an advantage because it reduces the number of parameters the network needs to learn, simplifying the model and making it more efficient.

💡Data Augmentation

Data augmentation is a technique used to increase the diversity of the training dataset by creating modified versions of the input data, such as rotated or scaled images. The video script uses this term to explain how CNNs can be trained to handle variations like rotation and scaling, which are common in real-world images.

💡Overfitting

Overfitting occurs when a model learns the training data too well, including its noise and details, to the extent that it negatively impacts the model's performance on new, unseen data. The video script discusses how techniques like pooling and ReLU can help reduce overfitting by making the model more generalizable.

Highlights

A simple explanation of convolutional neural networks (CNNs) is provided, making it accessible to high school students.

CNNs are used to recognize patterns such as handwritten digits, overcoming the issue of varying digit placement.

Traditional methods of image recognition using RGB values are too hard-coded and lack flexibility for variations.

Artificial neural networks (ANNs) are introduced to handle the variety in handwritten digit recognition.

ANNs face challenges with larger images due to the computational complexity of millions of weights.

The locality of image recognition is emphasized, as the position of features like a koala's face matters.

Neuroscience insights are applied to CNNs by mimicking how humans recognize images through distinct features.

Filters or feature detectors are used in CNNs to identify small features like edges and loops in digits.

The convolution operation is explained as a process to apply filters to an image to create a feature map.

Stride and filter size are discussed as parameters that influence the convolution operation.

Feature maps are highlighted as the result of convolution, showing the activation of certain features.

The concept of location invariance in feature detection is introduced, allowing for detection regardless of feature position.

Pooling operations, such as max pooling, are explained to reduce dimensions and computation in CNNs.

Benefits of pooling include reduced overfitting, dimension reduction, and tolerance to distortions.

The combination of convolution, ReLU (Rectified Linear Unit), and pooling forms the foundation of CNNs.

The fully connected dense neural network is used in CNNs for classification after feature extraction.

Data augmentation is suggested as a technique to handle rotation and scaling in CNNs.

The self-learning capability of CNNs is emphasized, as networks learn the optimal filters during training.

A summary of CNNs is provided, outlining the process from input image to classification.

The presenter, Daval Patel, introduces himself and his educational content on data science, machine learning, and programming.

Transcripts

i will give you a very simple

explanation of convolutional neural

network without using much mathematics

so that even a high school student can

understand it easily

let's say you want the computer to

recognize the handwritten digit

9. the way computer looks at this is

as a grid of numbers here i'm using -1

and 1.

in reality it will use rgb numbers

from 0 to 255.

the issue with this presentation is that

this is too much hard-coded

if you have a little shift in digit 9

for example

9 here was in the middle but in this

case it is in the left

and the representation of numbers just

changes

it doesn't matches match with our

original

number grid and computer will not be

able to

recognize that this is number nine

there could be a variation since it is a

handwritten digit

there could be variation in how you

write it

which will change the two-dimensional

representation of numbers

and again you will not be able to match

it with the original grid

so we use artificial neural network

for this kind of case to handle the

variety

in this deep learning series we have

already looked at

artificial neural network video on

handwritten digits

recognization if you are not seen that

video please make sure you see it so

that

your fundamentals on artificial neural

networks are clear

in that we created a one-dimensional

array by flattening the

two-dimensional representation of our

hand

written digit number and then we build a

neural network with

one hidden layer and output layer

and this dense neural network will work

okay for a simple

image like handwritten digit but when

you have a bigger image

let's see this little cute looking koala

the image size is 1920 by 1080

we have three as rgb channel here

one for red green and blue in this case

the first layer neuron itself will be

six million

if you have let's say hidden layer with

4 million neurons

you're talking about 24 million

weights to be calculated just between

the input and hidden layer

and remember deep neural networks have

many hidden layers so this can go

easily into like 500 million or 1

billion

of weights that you have to compute and

that's too much computation for your

little computer

see my rabbits are getting electrical

shock

because it's just too much to do

so the disadvantages of using a n or

artificial neural network for image

classification is

too much computation it also treats

local pixels same as pixels far apart

if you have koala's face in a left

corner versus right corner

it is still a koala doesn't matter where

the face is located

so the image recognization task is

centered around the locality

okay so if the pixels are moved around

it should still be able to detect the

object in an image but with a n it's

hard

so how does human recognize this image

so easily so let's go into the

neuroscience little bit

and try to see how we as humans

recognize

any image so easily when we look at

koala's

image we look at the little features

like this round eyes

this black prominent flat nose

this fluffy ears and we detect these

features

one by one in our brain

there are different set of neurons

working on these different

features and they're firing they're

saying yeah i found koala's ears

yes i found koala's nose and so on

then these neurons are connected to

another set of neurons

which will aggregate the results it will

say

if in the image you are seeing koalas

eye nose and ears

it means there is a koala's face in the

image

similarly if there is koala's hands and

legs

it means there is koala's body and there

are different set of neurons which are

connected

to these neurons which will again

aggregate the results saying that

if the image has koala's head and body

it means it is koala's image

same thing with handwritten digit nine

there are these little edges

which come together and form a loopy

circle pattern

which is kind of like a head of digit

nine

in the middle you have a vertical line

at the bottom you have a diagonal line

sometimes you don't have diagonal line

at all but

we know that whenever there is a loopy

circle

pattern at the top vertical line in the

middle

diagonal line in the end that means

digit nine

so how can we make computers recognize

these

tiny features we use the concept of

filter in case of nine

we have three filters the first one is

the

head which is a loopy circle pattern

in the middle you have vertical line in

the end you have diagonal filter

so we take our original image and

we will apply a convolution operation

or a filter operation so here i have a

loopy circle pattern or a head filter

this filter right here

the way convolution operation works is

you take three by three grid from your

original image

and multiply individual numbers with

this filter so this minus 1 is

multiplied with this one

this one is multiplied with this one and

so on

in the end you get a result and then you

find the average

which is divided by 9 because there are

total 9 numbers

and whatever number you get you put it

here

now this particular thing is called a

feature map so by doing this convolution

operation you are creating a feature map

so you do it for the second round of

three by three grid here i'm taking a

stride of

one you can take a stride of two or

three

also you don't need to have three by

three filter

you can have four by four or five by

five filter

and then you keep on doing this

for your entire number and in the end

what you get is called a feature map now

the benefit here is

wherever you see number one or a number

that is close to one

it means you have a loopy circle pattern

so this is

detecting a feature in the case of koala

this would be eye or a nose

because for koala i knows ears are the

features

so by applying loopy pattern detector i

got this one here in my feature map

i also call it the feature is activated

you know

it got activated here

for number six it will be activated in

the bottom in this area

if you have two loopy patterns the

feature will be activated at top and

bottom

if your number like this it might be

activated in different area

in summary when you apply this filter or

a convolution operation

you are generating a feature map

that has that particular feature

detected

so in a way filters are nothing but the

feature detectors

for koala's case you can have eye

detector

and when you apply convolution operation

in the result see

you got these two eyes at this location

if the eyes are at a different location

it will still detect because you are

moving the filter

throughout the image

and they are location invariant which

means doesn't matter

where the eyes are in the image these

filters

will detect those eyes and it will

activate those particular regions

here i have six eyes from three

different koalas

and they are activated accordingly great

the hand of koala is in this particular

region

for therefore when i apply hence

detector

it will activate here

now for number nine and i'm just moving

between number nine and koala so that

the presentation is simple enough and

you still get an idea

in case of nine we saw that we need to

apply three filters

the head the middle part and the tail

and when you apply those you get three

feature maps

so i apply three filters i got three

feature maps

and this is how these feature maps are

represented if you're reading any online

article

or a book they are kind of stacked

together

and they almost form a 3d volume

in case of koala my eye nose and

ear filters will produce three different

feature maps

and i can apply convolution operation

again and let's say this time the filter

is to detect head

by the way the filter doesn't have to be

2d

it can be three dimensional as well

so just imagine this first dimension

is representing eyes and the second

slice is representing nose and third

slice

representing ears and by doing that

filter

you can say that koala's head in

is in this particular region of an image

so you are aggregating this result using

a different filter for head

and now this becomes a koala head

detector

similarly there could be koala body

detector

and now we got these two new feature

maps

where this feature map is saying that

koala's head is at this location and

paula's body is at this particular

location

then we flatten these numbers see in the

end these are like

two dimensional numbers so we can

flatten them

so to convert 2d array into 1d array

and then when you get these two array

just join them together after you join

you can make a fully connected

dense neural network for your

classification

now why do we need this

fully connected network here well you

can have a different image of koala see

my koala is sleeping he's tired

so now his eyes and ears are at a

different location look at his ears

see they're here for previous image

the ears were in a different location

so that generates a different type of

flattened array here

and you all know if you know basics

about neural network that

neural networks are used to handle the

variety in your inputs

such that it can classify those variety

of inputs in a

generic way here the first part where we

use

convolutional convolution operation

is feature extraction part and the

second portion where we are using dense

neural network is called classification

because the first part is detecting all

the features ears nose eyes head and

body etc

and the second part is responsible for

classification

we also perform a value operation so

so this is not a complete convolutional

neural network

there are two other components one is

value

which is nothing but if you have seen my

activation

video on the same deep learning tutorial

series

we use erect value activation to bring

non-linearity in our model so what it

will do is

it will take your feature map and

whatever negative values are there

it just replaces that with zero it is so

easy

and if the value is more than zero it

will keep it as it is

so you see just look at the values it's

pretty straightforward

a value helps with making the model

non-linear because you are

picking bunch of values and making them

zero so if you see my previous videos in

this deep learning tutorial series

you will get an idea on why it brings

the non-linearity especially see the

video on

the activations in the same tutorial

series

the the link of this playlist is in the

in the video description below

so you'll understand why relu makes it

non-linear

but we did not address the issue of too

much computation yet

my rabbits are still getting electrical

shock

do something because see for this image

size

if you are applying convolution let's

say with some padding

you're still getting same size of image

you did not reduce the

image size sometimes people don't use

padding so they

reduce the image size but only little

bit

so pulling is used to reduce the size so

main purpose of pulling is to reduce the

dimensions so that

my computer doesn't get this shock you

know

so the first pulling operation is um

the max pulling so here you take a

window of 2x2

and you pick the maximum number from

that window and put it here

so here check this yellow window 5 1 8 2

what is the maximum number 8

so put 8 here here what is the maximum

number 9 so put 9 here

similarly here maximum number in green

window is three so put three

so you take the feature map apply your

convol

your pulling and generate a new feature

map

after the pulling but the new feature

map

is half the size if you look at the

numbers you know you have reduced your

16 numbers into four

so it's too much or saving in your

computation

so how it will look for our digit nine

case when you apply max pooling

well you can do a stride of one in this

case we did

two by two window and stride of two

start of two means

once we are done with this window we

move two points forward

for further two pixels further in this

case we can do one stride see this is

one stride

you get an idea and we keep on taking

max

and this is what we get when our number

is shifted so see this is the original

number where we got this max pooling

map when number is shifted you get

this pulling map so still you are

detecting the

loopy pattern at the top so max pulling

along with convolution

helps you with

position invariant feature detection

doesn't matter where your eyes or ears

are in the

image it will detect that feature for

you

there is average pooling also instead of

max you just make an average see

5 and 1 6 and 2 8 8 and 8 16.

16 divided by 4 is 4. so

but max pooling is more generally used

but sometimes people use average pooling

also

so benefits of pooling number one

obvious it's

reducing your dimension and computation

the second benefit is reduce

overfitting because there are less

parameters

and the third one is model is variant

tolerant towards variation and

distortion because if there is a

distortion

and if you're picking just a maximum

number you are capturing the

main feature and you are filtering all

the noise

so this is how our complete

convolutional neural network looks like

in that you will have typically a

convolution and value layer

then you will have pulling then there

will be another convolution value

pulling

there could be n number of layers for

convolution and pulling

and in the end you will have fully

connected dense neural

network in this particular case the

first

convolution layer is detecting eye nose

and ears

many times you will start with the

little edges you don't even start with

eye and nose but

here for the simplicity i have put them

but usually you start with edges then

you go to eye nose ears then you go to

head and body

and then you do flattening again

anything on the left hand side of this

vertical line is feature extraction

so the main idea behind convolutional

neural network is feature extraction

because the second part is same it is a

simple artificial neural network

but by doing this convolution you are

detecting the features

you are also reducing the dimension

there are three benefits of convolution

operation

the first one is connection sparsity

reduces overfitting connection sparsity

means

not every node is connected with

every other node like in artificial

neural network

where we call that a dense network

here we have a filter which we move

around the image

and at a time we are talking about only

a local region

so we are not affecting the whole image

the second benefit is

convolution and pulling operation

combined

gives you a location invariant feature

detection

which means koala's eye could be in the

left corner in the right corner

anywhere we will still detect it

third is a parameter sharing which is

when you learn the parameters for a

filter

you can apply them in the entire image

the benefit of value is that it

introduces non-linear

linearity which is essential because

when we are solving a

deep learning problems they are

non-linear by nature

it also speeds up training and it is

faster to compute

remember value is you are just doing one

check whether the number is greater than

zero or not

if it is greater than zero keep the

number less than zero make it zero

the benefit of pulling is that it

reduces dimension and computation

it reduces overfitting and makes the

model

tolerant to our small distortions

how about rotation and thickness because

by itself cnn cannot handle

the rotation and the thickness

so you need to have training samples

which have some rotated and scaled

sample you know some thick samples some

thin samples and if you don't you can

use

data augmentation technique what is data

augmentation

let's say for handwritten digits you

take your original data set

and then you pick few samples and then

you rotate them manually

or you make them larger or you make them

smaller

thicker or thinner and you generate new

samples

by doing that you can handle rotation

and scale

in convolutional neural network

once again here is a quick summary of

what is convolutional neural network you

can take a screenshot of this image

put it at your desk if you are trying to

learn cnn

and a computer vision to summarize

you take your input image then you apply

convolution operation and value

then you apply pulling again convolution

value pulling

and you can do this n number of times

after that the second stage

is classification where you use densely

connected neural network

now very important thing to mention here

is these filters the network will learn

on its own

in previous presentation we saw that we

applied

those filters by hand but this is the

beauty of convolutional neural network

that

it will automatically detect these

filters

on its own and that is part of the

training so when the neural network is

training or when the cnn is training

because

you're supplying thousands of koalas

images here

using that it will use back propagation

and it will figure out

the right amount of filters it will

figure out the values in this filter

and that is part of the learning or the

back propagation

as a hyper parameter you will specify

how

how many filters you want to uh have

and what is the size of each of the

filters that's it

but you do not specify the exit values

within these filters the network will

learn those

on its own and this is that is the most

fascinating part about

neural network in general in next few

videos

we will be doing coding using

convolutional neural network

and will be solving variety of computer

vision problems

so i hope you like this explanation if

you don't know me

i'm daval patel i teach

data science machine learning python

programming and career guidance

on my youtube channel if you are

starting machine learning

and if you are looking for a very basic

beginner's level of tutorials

then i have a complete playlist you can

start with

very basic python and pandas knowledge

on this playlist

and can learn machine learning in a very

very easy to understand manner

then gradually in this playlist i try to

cover

data science and machine learning

projects as well i'm continuing my deep

learning tutorial series right now

and my goal is to finish all the topics

in deep learning

including convolutional neural networks

rnns language models and so on so

please stay tuned uh watch my videos and

if you have any comments or feedback

please let me know in the video comment

below