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Difference between ANN, CNN and RNN

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Prerita Agarwal
Data Specialist @
23 Jul, 2024 @ 01:30 PM

Introduction

In artificial intelligence and machine learning, three fundamental architectures stand out: Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN). Each architecture brings its unique characteristics and applications, shaping the landscape of modern AI solutions. In this blog, we delve into the key differences between ANN, CNN, and RNN, exploring their structures, functionalities, and real-world use cases.

Difference between ANN, CNN and RNN

Source: Link

Key difference between ANN, CNN and RNN

The key difference lies in their architectures and applications:

  1. Artificial Neural Networks (ANN) are versatile, fully connected networks suitable for structured data and general-purpose tasks.
  2. Convolutional Neural Networks (CNN) excel at image recognition and processing, leveraging shared weights and local connectivity.
  3. Recurrent Neural Networks (RNN) specialize in sequential data, retaining memory through recurrent connections, making them ideal for tasks like natural language processing and time series analysis.
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What is deep learning?

Deep learning is the subdivision of machine learning which helps the computer learn new things without human intervention. It is based on various neural networks. 

Source: Link

Now the question is, what are neural networks? 

What are Neural Networks?

Neural networks are symbolized as the brain of deep learning. Like in a human brain, a message is transmitted with the help of neurons; the neural networks are also designed to work as the biological neuron to pass signals onto each other. It can find hidden patterns and correlations in raw data, cluster them, and classify them using numerous algorithms.  

Also see, Artificial Intelligence in Education

Artificial neural networks (ANN)

An artificial neural network is the simplest, as it passes information through multiple neurons until it generates an output. It is also known as a feed-forward neural network because the inputs are processed only in a forward direction.

Source: Link

As you can see in the image, ANN consists of three layers. The input layer inputs the variables, after which the hidden layer performs all the formulas and various expressions to process the output. 

Advantages and disadvantages of ANN

Advantages of Artificial Neural Networks (ANN):

  1. Versatility: ANNs can be applied to various tasks, including classification, regression, and pattern recognition, making them suitable for a wide range of problems.
  2. Adaptability: They can learn complex patterns and relationships from data without requiring explicit programming, allowing them to adapt to changing environments.
  3. Parallel processing: ANNs can perform computations in parallel, speeding up training and inference processes for large datasets.
  4. Fault tolerance: They exhibit graceful degradation, meaning they can still produce reasonable outputs even with damaged or missing neurons.

Disadvantages of Artificial Neural Networks (ANN):

  1. Black-box nature: ANNs often lack interpretability, making it challenging to understand the reasoning behind their decisions.
  2. Training complexity: Training ANNs can be computationally intensive and time-consuming, especially for deep architectures and large datasets.
  3. Overfitting: ANNs may memorize noise or irrelevant patterns in the training data, leading to poor generalization on unseen data.
  4. Need for large datasets: ANNs typically require large amounts of labeled data to achieve optimal performance, which may not always be available.

Source: Link

Convolution Neural Network (CNN)

Convolution Neural Network is a sort of Deep Learning algorithm that takes an image as input and learns the various features of the image through filters, also known as kernels. This allows the model to learn and catch up with the different objects present in the picture. For example, CNN will learn the features of apple that differ from orange so that when we provide apple input, it can differentiate between the two. 

Source: Link

Now let us see how does the CNN algorithm work.

Source: Link

As shown in the image, the first layer inputs the image dimensions, then in the hidden layers, the model learns various image features. Finally, the image classifier is ready for use. 

Advantages and disadvantages of Convolution Neural Network (CNN)

Advantages of Convolutional Neural Networks (CNN):

  1. Feature extraction: CNNs automatically learn hierarchical features from input data, making them effective for tasks like image recognition and computer vision.
  2. Spatial hierarchies: CNNs preserve the spatial structure of images through convolutional layers, capturing local patterns and spatial relationships.
  3. Parameter sharing: By sharing weights across different parts of an image, CNNs are more parameter-efficient and can generalize better to new data.
  4. Translation invariance: CNNs can detect features regardless of their position in the input, making them robust to translations, rotations, and other transformations.

Disadvantages of Convolutional Neural Networks (CNN):

  1. Complexity: CNN architectures can be complex, requiring careful design and tuning of hyperparameters, and may suffer from overfitting, especially on small datasets.
  2. Computational cost: Training CNNs can be computationally expensive, particularly for deep architectures and large datasets, limiting their scalability.
  3. Limited interpretability: While CNNs are effective for feature extraction, their black-box nature can make it challenging to interpret their decisions and understand the reasoning behind them.
  4. Data dependency: CNNs rely heavily on large amounts of labeled training data, which may not always be available or may require extensive preprocessing.

Recurrent Neural Network (RNN)

An RNN is a category of neural network where there is a connection between the nodes along a temporary sequence. The temporary sequence is the data that transitions over time. The most used example of RNN is Google assistance and Apple’s Siri. It is the algorithm that remembers and stores the input in internal memory, due to which this algorithm can solve all the problems having sequential data. 

Source: Link

Each node in the RNN model acts as a memory cell, saves the previous node’s output, and feeds the results rather than moving in a forwarding direction to give an output. If the model’s prediction is incorrect, it learns itself and continues working towards a better prognosis during backpropagation. 

Source: Link

Advantages and disadvantages of Recurrent Neural Network (RNN)

Advantages of Recurrent Neural Networks (RNN):

  1. Sequential data processing: RNNs excel at processing sequential data such as time series, speech, and natural language, where the order of input elements matters.
  2. Temporal dependencies: RNNs can capture temporal dependencies in data through recurrent connections, allowing them to retain memory and context over time.
  3. Variable input size: RNNs can handle input sequences of variable length, making them suitable for tasks where input lengths may vary.
  4. Flexibility: RNN architectures can be customized and adapted to different types of sequential data, offering flexibility in modeling diverse problems.

Disadvantages of Recurrent Neural Networks (RNN):

  1. Vanishing and exploding gradients: RNNs are prone to vanishing and exploding gradient problems, which can hinder training stability and convergence, especially in deep architectures.
  2. Short-term memory: Standard RNN architectures have limitations in capturing long-term dependencies, as information from earlier time steps may decay or become diluted over time.
  3. Training complexity: Training RNNs can be challenging due to issues like vanishing gradients, requiring specialized techniques such as gradient clipping and careful initialization.
  4. Sequential processing: RNNs process input sequences sequentially, limiting parallelization and potentially leading to slower training and inference times compared to other architectures.

ANN vs. CNN vs. RNN

Here is a table of comparison ANN vs. CNN vs. RNN:

Parameters

ANN

RNN

CNN

Data

Tabular data

Sequence data

Image data

Recurrent Connections

No

Yes 

No

Parameter sharing

No

Yes

Yes

Spatial relationship

No

No

Yes

Vanishing and exploding gradient

Yes

Yes

Yes

Loss Function

J=∑(youtput-ypredicted)

J=∑J(ŷ,y)

J(C,G)=1/2|a[l](C)-a[l](G)|2

Gradient Function

∑∂J/∂W

∂J/∂W = ∑∂J(T)/∂W

∂E/∂W = ∑∂ET/∂W

In the above ANN vs. CNN vs. RNN table,

J(C, G) represents how the generated image G differs from the original image C

Also see, Yarn vs NPM

When you should consider using other types of neural networks?

You should consider using other types of neural networks when the characteristics of your data or the requirements of your problem domain are better suited to alternative architectures. Here are some scenarios where other types of neural networks may be more appropriate:

Convolutional Neural Networks (CNNs):

  • Use CNNs for tasks involving structured grid-like data, such as images or audio spectrograms.
  • When your data exhibits spatial relationships and locality, CNNs can effectively capture these features through convolutional layers.
  • Tasks like image classification, object detection, and image segmentation often benefit from the hierarchical feature extraction capabilities of CNNs.

Recurrent Neural Networks (RNNs):

  • RNNs are suitable for sequential data with temporal dependencies, such as time series, natural language, and speech.
  • When your data has a time-based or sequential nature, RNNs can capture and model long-term dependencies and context.
  • Tasks like sentiment analysis, language translation, and speech recognition often rely on RNNs to process and understand sequential patterns.

Attention Mechanisms and Transformers:

  • Use attention mechanisms and transformer architectures for tasks requiring long-range dependencies and global context.
  • When your data has complex relationships across distant elements or requires capturing global context, transformers excel at attending to relevant information.
  • Tasks like language modeling, document summarization, and machine translation benefit from the attention mechanisms and self-attention layers in transformer architectures.

Graph Neural Networks (GNNs):

  • Consider GNNs for data represented as graphs or networks, such as social networks, molecular structures, or recommendation systems.
  • When your data exhibits relational structures and interactions between entities, GNNs can capture and propagate information across nodes and edges.
  • Tasks like node classification, link prediction, and graph generation often leverage GNNs to model complex graph-structured data.

Frequently Asked Questions

Why is CNN preferred more than RNN for image processing?

Recurrent Neural Network comprises less feature compatibility when compared to Convolution Neural Networks. CNN takes fixed-size inputs and generates fixed-sized outputs. RNN can handle arbitrary information or output lengths. CNN is ideal for images and video processing

Is RNN a type of ANN?

Yes, RNN (Recurrent Neural Network) is a type of ANN (Artificial Neural Network) specialized for processing sequential data.

Why is CNN better than ANN?

CNN (Convolutional Neural Network) is better than ANN for image processing tasks due to its ability to automatically learn hierarchical features and preserve spatial information.

What are the three different types of neural networks?

The three different types of neural networks are ANN (Artificial Neural Network), CNN (Convolutional Neural Network), and RNN (Recurrent Neural Network).

Conclusion

In this article, we have studied deep learning, a brief description of neural networks, the categories of neural networks. A detailed study about ANN vs. CNN vs. RNN: if you are interested in knowing more, check out our industry-oriented deep learning course curated by our faculty from Stanford University and Industry experts.

Topics covered
1.
Introduction
2.
Key difference between ANN, CNN and RNN
3.
What is deep learning?
4.
What are Neural Networks?
5.
Artificial neural networks (ANN)
5.1.
Advantages and disadvantages of ANN
6.
Convolution Neural Network (CNN)
6.1.
Advantages and disadvantages of Convolution Neural Network (CNN)
7.
Recurrent Neural Network (RNN)
7.1.
Advantages and disadvantages of Recurrent Neural Network (RNN)
8.
ANN vs. CNN vs. RNN
9.
When you should consider using other types of neural networks?
10.
Frequently Asked Questions
10.1.
Why is CNN preferred more than RNN for image processing?
10.2.
Is RNN a type of ANN?
10.3.
Why is CNN better than ANN?
10.4.
What are the three different types of neural networks?
11.
Conclusion