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Difference Between Deep Learning and Neural Network
Deep learning and neural networks are both machine learning methods that are used to identify patterns and make predictions. While the two terms are often used interchangeably, there are important differences between them that can have significant implications for their use.
What is Deep Learning?
Deep learning is a broader category of machine learning that encompasses neural networks and other approaches. Deep learning involves training models to recognize patterns in data by processing multiple layers of information. These models can learn from vast amounts of data and can recognize patterns that are too complex for humans to identify.
What is Neural Network?
The term "Neural Networks" is used to describe a system of virtual neurons or nodes that is loosley modelled after the neural networks that make up the brains of various animals. A lot of today's AI has its roots in this technique. In fact, research indicates that the current implications and applications of AI are just the result of the evolution of the special qualities of neural networks (such as machine learning, deep learning, etc.).
Computer science, physics, information science, psychology, and engineering all have a hand in developing and refining the neural network paradigm. Neural networks are networks of nodes whose functioning is inspired by animal neurons but only in a very general way. Neural networks are widely employed in many fields today, from issue solving and consumer research to data validation and sales forecasting and risk management.
Differences: Deep Learning and Neural Network
One of the key differences between neural networks and deep learning is their complexity. Neural networks are relatively simple compared to deep learning models. They typically consist of a single layer of neurons that are connected to each other. These networks are effective at recognizing simple patterns in data, but they are not able to handle complex data sets.
Deep learning models, on the other hand, can process multiple layers of data and can recognize complex patterns that are not immediately visible to humans. This makes them ideal for applications like image and speech recognition, where the data is highly complex and requires sophisticated processing.
Another important difference between neural networks and deep learning is the way that they are trained. Neural networks are typically trained using a process called backpropagation, which involves adjusting the weights of the connections between neurons based on the error between the predicted output and the actual output.
Deep learning models, on the other hand, are trained using a process called gradient descent, which involves adjusting the weights of the connections between layers of neurons based on the gradient of the error function. This allows the model to learn more complex patterns in the data and to make more accurate predictions.
The following table highlights the major differences between Neural Network and Deep Learning −
Characteristics |
Neural Network |
Deep Learning |
|---|---|---|
Concept |
Neural network, also called artificial neural network, is an information processing model that stimulates the mechanism of learning biological organisms. It is inspired by the idea of how the nervous system operates. The nervous system contains cells which are referred to as neurons. Similarly, neural networks consist of nodes which mimic the biological function of neurons. |
Deep learning, on the other hand, is much broader concept than artificial neural networks and includes several different areas of connected machines. Deep learning is an approach to AI and a technique that enables computer systems to improve with experience and data. |
Architecture |
Neural networks are simple architectural models based on how the nervous system works and are divided into single-layer and multi-layer neural networks. The simple instantiation of a neural network is also referred to as the perceptron. In the single-layer network, a set of inputs is mapped directly onto an output using generalized variation of a linear function. In multi-layer networks, as the name suggests, the neurons are arranged in layers, in which a layer of neutrons is sandwiched between the input layer and output layer, which is called the hidden layer. |
Deep learning architecture, on the other hand, is based on artificial neural networks. |
Applications |
Neural networks allow modeling of non-linear processes, so they make great tools for solving several different problems such as classification, pattern recognition, clustering, prediction and analysis, control and optimization, machine translation, decision making, machine learning, deep learning and more. |
Deep learning models can be applied to various fields including speech recognition, natural language processing, self-driving vehicles, computer-aided diagnosis, voice assistant, sound creation, robotics, computer games, image recognition, brain cancer detection, social network filtering, pattern recognition, biomedicine, and more. |
Conclusion
The main difference between deep learning and neural networks is the complexity of the models and the way that they are trained. Neural networks are simpler and more limited in their capabilities, while deep learning models are more complex and can handle more complex data sets.
Both approaches have their strengths and weaknesses, and the choice between them will depend on the specific application and the type of data that is being analyzed.
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