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Differences between neural networks and deep learning


Since their inception in the late 1950s, artificial intelligence and machine learning have made significant advancements. These technologies are now incredibly complex and cutting-edge. While technological advancements in the field of data science are certainly beneficial, they have also given rise to a number of terminologies that are obscure to the average person.

That is why we frequently observe and hear the words "Artificial Intelligence," "Machine Learning," and "Deep Learning" being used interchangeably by others around us. Despite the conceptual resemblances, each of these technologies is distinctive in its own manner. 

Today, we'll talk about the Deep Learning vs. Neural Network controversy, one of the less-discussed topics in data science.

How does deep learning work?

Deep Learning, also known as hierarchical learning, is a branch of machine learning used in artificial intelligence that can mimic the way the human brain processes data and develops patterns that are comparable to the ones the brain uses to make judgments. Deep Learning systems learn from data representations, as opposed to task-based algorithms. They may learn from unstructured or unlabeled data.

Neural networks: what are they?

A collection of algorithms that are based on the human brain make up a neural network. These algorithms have the ability to label or cluster the raw data and interpret sensory data using machine perception. In order to represent all of the real-world data (images, sound, text, time series, etc.), they are designed to recognize numerical patterns inherent in vectors.

Neural network vs. deep learning:

Although Deep Learning incorporates Neural Networks into its architecture, Deep Learning, and Neural Networks are fundamentally different from one another. We'll clarify the three main distinctions between Deep Learning and Neural Networks in this section.

  1. Definition:

Artificial neurons act as the primary processing unit of neural networks, a structure made up of machine learning (ML) algorithms that focus on exposing hidden patterns or relationship connections in a dataset, much in the way, the human brain does when making decisions.

Deep Learning is a subset of machine learning course that uses numerous layers of nonlinear processing units for information extraction and manipulation. It performs the ML process using numerous layers of artificial neural networks. 

  1. Structure:

In a neural network, there are the following elements:

  • A mathematical function called a neuron is created to mimic the operation of a biological neuron. It runs the data through a nonlinear function and calculates the weighted average of the input data.
  • Weights and connections - As the name implies, connections link neurons in the same or a different layer together. There is a weight value associated with each connection.
  • In a neural network, there are two propagation functions at work: forward propagation, which transmits the "predicted value," and backward propagation, which transmits the "error value."

The following elements make up a deep learning model:

  • PCI-e lanes are typically the foundation of the motherboard chipset of the model.
  • Processors - The GPU needed for Deep Learning must be chosen based on the processor's cost and number of cores.
  • RAM is the term for the actual storage and memory. The RAM must be enormous since Deep Learning algorithms require more CPU and storage space.
  • PSU - As memory requirements rise, it is essential to choose a sizable PSU that can perform large-scale and intricate Deep Learning operations.

  1. Architecture:

  • The most prevalent type of neural network architecture, called feed-forward neural networks, has the input layer as the first layer and the output layer as the last. Hidden layers make up all intermediate layers.
  • In recurrent neural networks, the connections between the nodes create a directed graph over a temporal series. This kind of network so represents temporal dynamic behavior.
  • The sole distinction between symmetrically connected neural networks and recurrent neural networks is that symmetrically connected neural networks feature connections between units that are equal in weight in both directions. As opposed to a recurrent neural network, they have more limitations because they use energy functions.

  1. Time and precision:

In general, neural network training takes less time. When compared to deep learning methods, they have poorer accuracy. Deep learning model training requires more time. When compared to neural networks, they have superior accuracy. This is the key distinction between neural networks and deep learning.

  1. Critique:

Theoretical issues, training issues, hardware issues, hybrid methodologies, and real-world criticism examples all play a role in neural network criticism. The criticism of deep learning, on the other hand, is based on mistakes, theories, online threats, etc. This distinction between deep learning and neural networks enables you to choose the best model for a given situation.

  1. Interpreting the task:

Deep learning networks read your tasks more accurately than neural networks, which do so badly.


It is difficult to tell Deep Learning and Neural Networks apart on the surface level because they are so closely related to one another.  But at this point, you've realized that Deep Learning and Neural Networks differ significantly from one another.

Check out our Differences Between Deep Learning vs Neural Networks for working professionals if you're curious to learn more about deep learning vs. neural networks.

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