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Introduction
Machine learning, or ML, is a subfield of artificial intelligence that uses statistical methods to solve huge volumes of data without the need for humans. Machine learning solves problems in the same way that humans do but with vast amounts of data and automated procedures. Deep Learning and CNN are the types of Machine Learning.
This blog will discuss the topic of Deep Learning vs CNN in detail. Let's start with the definition of Deep Learning.
Deep Learning
Deep Learning is a sub-part of Machine Learning that uses Artificial Neural Networks (ANNs) to replicate the features of the human brain. It needs a huge dataset to accurately learn characterizing features of the given input. Deep Learning needs quite a long time to train its model.
The above image explains the working of Deep Learning, where the input layer learns and deviates from each hidden layer to give the output. Deep Learning is a complex type of engineering. And this comes at a high market cost. Deep Learning algorithms are indeed more expensive than other electronic tools.
Features of Deep Learning
Let us now discuss the features of Deep Learning.
Deep Learning has a feature called Learning rate decay. This is a type of hyperparameter learning which means a factor that sets the conditions of data cleaning before training them for the model
It offers the learning transfer to the existing data network
It collects a large data set from scratch that helps the model to predict the output precisely
Users can solve the issue of overfitting at any stage using dropdown. It also boosts the performance of the neural network
Advantages of Deep Learning
We will now discuss the advantages of Deep Learning.
Deep Learning is highly reliable if implemented correctly
It also doesn't require preprocessing of data
Users can process both structured and unstructured data using Deep Learning
Disadvantages of Deep Learning
As we have now discussed the advantages, we will now move to the disadvantages of using Deep Learning.
Deep Learning requires a huge amount of data in the training phase
The computational cost incurred is high
The issue of biases is one of the major issues in Deep Learning
The learning rate is slow in Deep Learning
CNN
A CNN (Convolutional Neural Network) is a Deep Learning algorithm. It takes in an input image, assigns values to various aspects of the picture, and differentiates one from another. The preprocessing needed in a CNN is much lower as compared to different classification algorithms.
A CNN can successfully store the Spatial and Temporal domain in an image through relevant filters. It performs a better fitting of the image due to the reduced number of features and the reusability of weights, which means the network can be trained to understand the image's fineness better.
Features of CNN
Let us now discuss the features of CNN.
CNN has designing and training networks
It uses a pre-trained model for transfer learning
CNN works with thousands or over a million images, which can take too much time, but CNN has GPUs that can increase the speed of this process
Advantages of CNN
Let us discuss some advantages of CNN.
By the face detection method in CNN, social media platforms suggest who might be in a photograph. This makes it easier to tag friends in photo albums
With the help of CNN, doctors can identify cancerous tumours incorporated into radiology technology. It also enables doctors to identify healthy anatomy better
Some platforms have included image searching options that allow brands to recommend items using CNN
As technology grows, driverless cars are trained in features like lane line detection to improve driver and passenger safety
Disadvantages of CNN
As we have now discussed the advantages, we will now move to the disadvantages of using CNN.
CNN can recognise patterns, but it fails when it comes to understanding an image's contents
CNN cannot classify images with different positions accurately
CNN faces challenges while getting adversarial examples means images with slight changes
CNN is slow due to operations like max pool
Similarities between Deep Learning and CNN
Deep learning is a tool to analyze big data using complex algorithms and artificial networks to train a model. This lets the model learn from their experience and recognize data/images like humans. CNN is a type of Deep learning which is used for object recognition and classification. Hence, Deep learning uses CNN to recognize objects in an image.
Deep Learning vs CNN - Table
We are now done with the basic concepts of Deep Learning and CNN. Let us now see the Deep Learning vs CNN in tabular form.
Deep Learning
CNN
Deep Learning is a sub-part of Machine Learning that uses ANNs to replicate the features of the human brain.
CNN is a kind of neural network model which is inspired by the human brain. CNN is made up of many neurons which are interconnected.
The performance of Deep Learning is high as compared to CNN.
The performance of CNN is low compared to Deep Learning.
It is a type of recursive neural network.
It is a type of feed-forward neural network.
It takes quite a long time to train the model.
It takes less time to train a model.
Deep learning is used in speech recognition, computer games, and more.
CNN is used in classification, prediction and analysis, clustering, and more.
Frequently Asked Questions
What is Deep Learning?
Deep Learning is a sub-part of Machine Learning that uses Artificial Neural Networks (ANNs) to replicate the features of the human brain. It needs a huge dataset to learn characterizing features of the given input accurately.
What is CNN?
A CNN (Convolutional Neural Network) is a Deep Learning algorithm. It takes in an input image, assigns values to various aspects of the picture, and differentiates one from another.
What is the main advantage of Deep Learning?
Deep Learning is highly reliable if implemented correctly. Also, it also doesn’t require preprocessing of data and can process both structured and unstructured data.
Conclusion
This article discusses the topic of Deep Learning vs CNN. In detail, we have seen the definition of Deep Learning and CNN. Along with this, we have seen the pros and cons of them and Deep Learning vs CNN in tabular form.
We hope this blog has helped you enhance your knowledge of Deep Learning vs CNN. If you want to learn more, then check out our articles.
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