Hello guys, In this blog post we are going to learn about the Convolutional Neural Networks in details which is nothing but a subset of the deep learning algorithms which also known as CNNs. It is specially having the importance in the field of the computer vision. It is much more important at the time of recognising the patterns in the forms of an image and majorly for such tasks like an image classification, detection of products or an object and also in the medicle field where the medicle image is going to analysis.
So in this blog post we will highlight important key components of the Convolutional Neural Networks and also going to explain that all topics how they works in very simple words.
If we talk about the various types of Neural Networks that are using for the different purposes, for example We want to predict the sequence of the words of like anything related that then we are using the Recurrent Neural Networks (RNNs) like that same for the image classification, We are going to use the Convolution Neural networks (CNNs).
Table of Contents
What is a Convolutional Neural Networks?
A Convolutional Neural Networks is a type of an ANN (Artifical Neural Networks) which was designed to process the grids of the data such as images. Before that neural networks are facing high dimensional inputs like an images becuase of that an every pixel of the image are independently. It has also one of the major advantage that is all about the structure, where the images are process with the more effiectively because it’s process spatially. For an example you could take of images and the videos where those patterns are playing the very special role.
Also Read: Perceptron Model
Architecture of CNN
So now let we proceed with an architecture of the CNN (Convolutional Neural Networks) having the many layers such as an input layer, convolutional layers, fully connected layers and the pooling layers.
The convolutional layers applies different filters to the input images to an extract the features as described in the image, it is pooling the samples of the image so it will reduce the computation and the fully connected layer decides the final prediction. Neural network learns by the different algorithm like the gradient descent and through the backpropagation for specially the optimal filters.
How Does a CNN Work?
Let’s go through the steps so we can understand that how CNNs is processing an image:
Input Image: Let’s we suppose the one image of the dog which represents as the matrix of that pixels values.
Convolutional Layer: The first layer applies the many filters to the image and each of the filter has might detcat te different functions such as the patterns, textures, edges and more.
ReLU Activation: This activation function is applies to the function or the feature maps which makes by the convolutional layer which are going to inroducing the non linearlity.
Pooling Layer: This layer reducing the spatial dimensions of the features maps which has the most important data while the reducing the load.
After that, this process of the convolution, activation and the pooling the repeating the multiple times and the each of the time extracting the more and more complex features or the functions from the image.
Fully Connected Layers: The final feature maps of the layer are rounded into a single vector and after that are passes through the fully connected layers. So these layers are combine the extracted features so that can be make a final decision.
Output Layer: An output layer gives a probablity distribution over the classes which is possible like for an example (DOG, KITES, BIRDS,…etc), and the class with the highest probablity is been chosen for the prediction.
Applications of CNNs
CNNs are having the wide range of application in the field of nerual networks so let we take some of the best suited example of that so you can get it where exastly it is using:
- Image Classification: It is used in image classfication for the identifying the object in the images.
- Image Segmentation: In image segmentation process, it is dividing the images into the segments so it can identify the various objects in that image easily.
- Face Recognition: Using CNNs, We can easily indetify facial features of the face.
- Medicle Places: It is analyzing the medicle images and after that it is used the identify the various entity of the deases or the abnormalities.
- Machine Learning: It is processing the visual data such as images & videos for an analyzing the parameters of that data so machine can easily trained by that features.
So these are the some basic examples where the CNNs are using mostly, There are wide range of applications are there where the CNNs are using.
Conclusion
Convolutional Neural Networks have changed the way we approach image processing problems, enabling outstanding accuracy in many different kinds of applications. CNNs could automatically learn to identify and recognize complex patterns simply by making use of picture spatial structure and implementing a sequence of convolutional, activation, and pooling layers. As scientific knowledge advances, we should expect more creative and powerful uses of CNNs in the future.
Understanding the fundamentals of CNNs is an essential step in applying what they can do in your own applications. Whether you’re working on an assignment for yourself or doing advanced research, the ideas covered in this post can act as a solid foundation for your study of convolutional neural networks.
Still if you have any query related this so you can comment or get connect with us, we will get back you soon as soon as possible and stay tuned for an amazing tech related stuffs.
Frequently Asked Questions
1.What is a Convolutional Neural Network (CNN)?
- Answer: Convolutional Neural Network is a type of deep learning model. It is designed to process grid like data. CNNs are most effective at recognizing patterns in visual data.
2. How does a CNN different from a traditional neural network?
- Answer: Traditional neural networks treats every input independently, while the CNNs will take an advantage of the spatial structure of the data.
3. What is the role of the convolutional layer in a CNN?
- Answer: The convolutional layer is the core component of a CNN. It is applying the filters to the input image to create the feature maps.
4. What is the purpose of the ReLU activation function in CNNs?
- Answer: The Rectified Linear Unit activation function gives the non-linearity into the CNN which enabling it to learn complex patterns.
5. Why are pooling layers used in CNNs?
- Answer: Pooling layers are reduce the spatial dimensions of the feature maps. This makes the CNN more efficient in recognizing objects even like if they are slightly shifted.
6. How do CNNs handle different image sizes?
- Answer: CNNs can handle different image sizes by using the different techniques such as padding, which adds an extra pixels around the edges of the image and global pooling, which is reduces the feature maps.
7. What are some common applications of CNNs?
- Answer: CNNs are mostly used in image classification, object detection, image segmentation, face recognition, and an analyzing medical images.
8. How do I get started with implementing a CNN?
- Answer: You can use the popular deep learning frameworks like the TensorFlow, Keras, or PyTorch. These libraries mostly provides the pre-built functions and layers that make it easier to construct, train, and deploy the CNN models.
