Difference Between Deep Learning and Neural Network (With Table)

With advancements in technology, we have discovered newer ways and methods that help us in solving our problems. Although technology and development involving technology have helped in making our lives easier, with the introduction of newer terms, the confusion in understanding their literal meaning and differentiating between them has become a challenging task for us. The same is the scenario with the terms: Deep learning and Neural network. They are often misinterpreted and used falsely.

Deep Learning vs Neural Network

The difference between deep learning and Neural networks is that deep learning is defined as a deep neural network that is made up of many different layers, and each layer comprises many different nodes. A Neural network helps you in performing your task with less accuracy, while in deep learning, due to the presence of multiple layers, your task is completed with efficacy. A Neural network requires less time to train the network as it is less complicated, while you may require a lot of time for training your deep learning network.

Deep learning is a subset in machine learning that gives the system the capability to function like a human brain and imitate patterns that our brain does for making decisions. A deep learning system learns from observing different kinds and patterns of data and drawing conclusions based on them. Deep learning is a deep neural network that is made up of many different layers, and each layer comprises many different nodes.

Neural networks are based on algorithms that are present in our brain and help in its functioning. A Neural network interprets Numerical patterns which may be present in the form of Vectors. These vectors are translated with the help of neural networks. The principal work that a neural network performs is the classification and grouping of data based on similarities. The most important advantage about a neural network is that it can easily adapt itself to the changing pattern of output, and you needn’t modify it every time based on the input that you provide.

Comparison Table Between Deep learning and Neural Network

Parameters of ComparisonDeep LearningNeural Network
DefinitionDeep learning is a subset of machine learning that gives the system the capability to function like a human brain and imitate patterns that our brain does for making decisions Neural networks are based on algorithms that are present in our brain and help in its functioning. A Neural network interprets Numerical patterns which may be present in the form of Vectors
Architectures1. Convolutional Neural Network
2. Recurrent Neural Network
3. Unsupervised Pre Trained Network
4. Recursive Neural Network
1. Recurrent Neural Network
2. Symmetrically connected Neural Network
3. Single-Layer Feed-Forward Network
Interpretation PowerThe deep learning network interprets your task with higher efficacy. A Neural network interprets your task with poor efficacy.
Components InvolvedLarge PSU, GPU, Huge RAM Neurons, learning rate, Connections, Propagation functions, weight
Time TakenIt may take a lot of time to train the network. Since it is less complex, the time required to train the network is very less.
PerformanceHigh Performance Low performance

What is Deep Learning?

Deep learning is a subset of machine learning that provides the system with the ability to function like a human brain and imitate patterns that our brain does for making decisions. A deep learning system learns from observing different kinds and patterns of data and drawing conclusions based on them. Deep learning is a deep neural network that is made up of many different layers, and each layer comprises many different nodes.

The various components of a deep learning system are a large PSU, GPU, and a huge RAM. Since the build-up of this network is rather complicated, it takes a lot of time and effort to train the network. The architectures that form the basis of Deep learning are Convolutional Neural networks, Recurrent Neural networks, Unsupervised Pre Trained Networks, and the Recursive Neural Network.

What is Neural Network?

Neural networks, as the name suggests, are based on the functioning of neurons present in the human body. This system works similarly to a chain of neurons that receive information and process it in humans. Neural networks are based on algorithms that are present in our brain (the neurons) and help in its functioning.

A Neural network interprets Numerical patterns which may be present in the form of Vectors. These vectors are translated with the help of neural networks. The principal work that a neural network performs is the classification and grouping of data based on similarities. The most important advantage about a neural network is that it can easily adapt itself to the changing pattern of output, and you needn’t modify it every time based on the input that you provide.

Main Differences Between Deep Learning and Neural Network

  1. Deep learning is a complex form of neural network. There are many different layers in a deep learning network which makes it way more complex than a Neural network.
  2. A deep learning system provides you with high efficiency and performance for the completion of your tasks, while a neural network performs tasks with low efficiency when compared to a deep learning system.
  3. The major components in a deep learning unit are Large PSU, GPU, and a Huge RAM, while that of a neural network are Neurons, learning rate, Connections, Propagation functions, and weight.
  4. Deep learning networks being complex, requires a lot of time to train the network, while a neural network requires comparatively very little time to train the network.

Conclusion

There is a lot of similarity between deep learning and neural networks, and hence it becomes a difficult task to differentiate between the two at times. On the one hand, neural networks complete their tasks with the help of neurons. Deep learning is based on observing a given set of data and drawing conclusions based on the same. The architectural build-up and functioning of these systems varies deeply and is the main point that differentiates these two.

References

  1. https://www.nature.com/articles/nature14539
  2. https://idea-stat.snu.ac.kr/book/2017%20neural%20network/20170814/ch8~11.pdf
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