Simple explanation of convolutional neural network | Deep Learning Tutorial 23 (Tensorflow & Python)
Summary
TLDRThis 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
🧠 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.
🔍 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.
🌐 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.
🔧 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.
🛠 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.
Mindmap
Keywords
💡Convolutional Neural Network (CNN)
💡Feature Map
💡Filter or Kernel
💡Stride
💡ReLU (Rectified Linear Unit)
💡Pooling
💡Fully Connected Layer
💡Backpropagation
💡Parameter Sharing
💡Data Augmentation
💡Overfitting
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
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