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 misinterpreted and used falsely.
Key Takeaways
- Neural networks are a type of machine learning that uses algorithms to recognize patterns and solve problems.
- Deep learning is a subfield of neural networks that uses multiple layers to process complex data.
- Neural networks can solve many problems, while deep learning is beneficial for processing image, speech, and text data.
Deep Learning vs Neural Network
The difference between deep learning and Neural networks is that deep learning is defined as a deep neural network comprising many different layers, and each layer comprises many different nodes. A Neural network helps you perform your task with less accuracy, while in deep learning, due to 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 to train your deep learning network.
Deep learning is a subset of machine learning that allows the system to function like a human brain and imitate patterns our brain uses to make 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 of 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
| Parameters of Comparison | Deep Learning | Neural Network |
|---|---|---|
| Definition | Deep 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 |
| Architectures | 1. 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 Power | The deep learning network interprets your task with higher efficacy. | A Neural network interprets your task with poor efficacy. |
| Components Involved | Large PSU, GPU, Huge RAM | Neurons, learning rate, Connections, Propagation functions, weight |
| Time Taken | It 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. |
| Performance | High 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 Recursive Neural Networks.
What is a 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 of 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
- Deep learning is a complex form of neural network. A deep learning network has many different layers, making it way more complex than a Neural network.
- 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.
- The major components in a deep learning unit are a Large PSU, GPU, and a Huge RAM, while that of a neural network are Neurons, learning rate, Connections, Propagation functions, and weight.
- 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.
- https://www.nature.com/articles/nature14539
- https://idea-stat.snu.ac.kr/book/2017%20neural%20network/20170814/ch8~11.pdf
Last Updated : 11 June, 2023
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Sandeep Bhandari holds a Bachelor of Engineering in Computers from Thapar University (2006). He has 20 years of experience in the technology field. He has a keen interest in various technical fields, including database systems, computer networks, and programming. You can read more about him on his bio page.
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