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Introduction
As we all know today, Artificial intelligence has gotten complex as well as advanced in recent years. While technological advances in the field of Data Science are admirable, they have resulted in a deluge of terminologies that are above the understanding of an average person. We have seen many times that the term Machine learning, deep learning, and artificial intelligence are used interchangeably. This is because they have multiple concepts with multiple names, some of which are entwined. But, all these terminologies in itself are unique and capable.
So, in this article, Deep Learning vs Neural Networks, we will discuss them and see their differences. So, Let's get started!
What are Neural networks?
Neural networks are the ones that are inspired by the most complicated and complex object - the human brain. We all know that the human brain is made up of neurons. The simplest computing unit in every neural network, including the brain's neural network, is a neuron. Neurons take input, process it and transfer it to other neurons in the multiple hidden layers of the network until the process output is reached the Output layer.
Neural networks are algorithms that can explain sensory data through a machine point of view and group or label the raw data. They are designed to determine numerical patterns stored in vectors that require the transformation of all real-world data. An artificial neural network has three layers: the input, output, and hidden layer.
Types of Neural Network
FeedForward Neural Networks: This is the most prevalent kind of neural network architecture; it has an input layer at the top and an output layer at the bottom. Middle layers are always hidden
Symmetrically connected neural networks: The main distinction between symmetrically linked neural networks and recurrent neural networks is that the connections between the units in symmetrically connected neural networks have the same weight in both directions
Recurrent neural networks: This network architecture consists of several ANNs connected to form a directed graph over a temporal timeline. As a result, this kind of network displays dynamic behavior over time
What is Deep Learning?
Deep learning is a subset of Machine learning that can copy the computing capabilities of the human brain and generate patterns that are used by the human brain for making certain decisions. Deep learning learns from data representation which is opposite to task-based algorithms. Deep learning can learn from unlabeled or unstructured data.
A deep neural network or deep learning system is basically a neural network with various hidden layers and multiple nodes in every hidden layer. Deep learning is the creation of deep learning algorithms that can be used for training and predicting output from complex input.
Types of Deep Learning
Unsupervised Pre-trained Network: As the name imply, this architecture is pre-trained based on prior experiences and does not require formal training. These consist of Deep Belief networks and Autoencoders
Recursive Neural Network: This is created by repeatedly applying the identical set of weights to a structured input and transfering a topological structure for generating a structured prediction on variable-sized input structures
Convolutional Neural Network: This deep learning system can take an input image, give different items in the image meaning (using learnable weights and biases), and then distinguish between these objects.
Deep Learning vs Neural Network Table
Difference Between
Deep Learning
Neural Networks
Definition
Deep learning is differentiated from neural networks based on their number of hidden layers or depth.
A model of neurons encouraged by the human brain is called Neural Networks. Many neurons interconnect with each other to form neural networks.
Components
PSU, RAM, Motherboards, Processors.
Propagation function, Neurons, Learning rate, Connection and weights.
Performance
It gives better performance compared to neural networks.
Performance is low when compared to Deep Learning.
Time & Accuracy
The time taken to train them is usually more. And they have accuracy compared to neural networks.
The time taken to train them is generally less. And they have low accuracy compared to Deep learning.
Deep learning interprets our tasks more effectively compared to neural networks.
Neural networks poorly perceive our tasks.
Applications
Pattern recognition, speech recognition, computer games, self-driving cars, natural language processing, social network filtering, etc.
Pattern recognition, prediction, classification, machine learning, clustering, decision-making, etc.
Frequently Asked Questions
What is deep learning?
Deep learning is a branch of machine learning that uses artificial neural networks to learn from data. The human brain inspires neural networks, and they can understand complex data patterns that would be difficult or impossible to learn with traditional machine learning algorithms.
What are neural networks?
Neural networks are a type of machine-learning model that is inspired by the human brain. They are made up of interconnected nodes, which are similar to neurons in the brain. Neural networks can understand to perform complex tasks by being trained on data.
What are the challenges of deep learning?
Deep learning can be computationally expensive, and it can be difficult to train neural networks on large datasets. Additionally, neural networks can be sensitive to the quality of the data they are trained on.
Conclusion
One thing is clear from this article that both deep learning and neural network are not exactly the same things. As they are closely related, it's difficult to tell which is what on the surface. This article has clearly described both topics and the difference between them; we hope you now have a clear understanding of Deep Learning vs Neural Networks.
To better understand the topic, you can refer to the following articles:
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