1. Submitted by : Namami Rajput
Submitted to : Mr. Juned Sir
PROJECT
2. Convolutional Neural Network
(CNN or ConvNet)
DEFINATION : A CONVOLUTIONAL
NEURAL NETWORK (CNN OR CONVNET) IS A
NETWORK ARCHITECTURE FOR DEEP
LEARNING THAT LEARNS DIRECTLY FROM
DATA.
CNNS ARE PARTICULARLY USEFUL FOR
FINDING PATTERNS IN IMAGES TO RECOGNIZE
OBJECTS, CLASSES, AND CATEGORIES. THEY
3. How CNNs Work?
A convolutional neural network can have tens or
hundreds of layers that each learn to detect different
features of an image. Filters are applied to each training
image at different resolutions, and the output of each
convolved image is used as the input to the next layer.
The filters can start as very simple features, such as
brightness and edges, and increase in complexity to
features that uniquely define the object.
4. Convolutional neural networks are distinguished from other
neural networks by their supe. rior performance with image,
speech, or audio signal inputsThey have three main types of
layers, which are:
Convolutional layer
Pooling layer
Fully-connected (FC) layer
The convolutional layer is the first layer of a convolutional
network. While convolutional layers can be followed by
additional convolutional layers or pooling layers, the fully-
connected layer is the final layer. With each layer, the CNN
increases in its complexity, identifying greater portions of the
image. Earlier layers focus on simple features, such as colors
and edges. As the image data progresses through the layers of
the CNN, it starts to recognize larger elements or shapes of the
object until it finally identifies the intended object.
5. Neural Networks: Layers and Functionality
In a regular Neural Network there are three types of layers:
Input Layers: It’s the layer in which we give input to our model. The
number of neurons in this layer is equal to the total number of features in
our data (number of pixels in the case of an image).
Hidden Layer: The input from the Input layer is then fed into the hidden
layer. There can be many hidden layers depending on our model and data
size. Each hidden layer can have different numbers of neurons which are
generally greater than the number of features. The output from each
layer is computed by matrix multiplication of the output of the previous
layer with learnable weights of that layer and then by the addition of
learnable biases followed by activation function which makes the network
nonlinear.
Output Layer: The output from the hidden layer is then fed into a logistic
function like sigmoid or softmax which converts the output of each class
into the probability score of each class.
Neural Networks: Layers and Functionality
6. Convolution Neural Network
Convolutional Neural Network (CNN) is the
extended version of artificial neural
networks (ANN) which is predominantly
used to extract the feature from the grid-
like matrix dataset. For example visual
datasets like images or videos where data
patterns play an extensive role.
7. Convolutional Neural Network consists of multiple
layers like the input layer, Convolutional layer, Pooling
layer, and fully connected layers.
CNNAr
chite cture
Simple CNN architecture
8. How Convolutional Layers Works?
• Convolution Neural Networks or covnets are neural networks
that share their parameters. Imagine you have an image. It
can be represented as a cuboid having its length, width
(dimension of the image), and height (i.e the channel as
images generally have red, green, and blue channels).
9. Let’s consider an image and apply the
convolution layer, activation layer, and pooling
layer operation to extract the inside feature.
Input image:
Example: Applying CNN to an Image
