Deep Learning vs Neural Networks: Difference Between Deep Learning and Neural Networks

Artificial Intelligence and Machine Learning have come a long way since their conception in the late 1950s. Today, these technologies have become immensely sophisticated and advanced. However, while technological strides in the Data Science domain are more than welcome, it has brought forth a slew of terminologies that are beyond the understanding of common man.

In fact, even many businesses leveraging disruptive technologies like AI and ML cannot tell apart many technological terminologies. 

The core cause of confusion around the new terminologies brought about by Data Science is because Data Science concepts are deeply entwined with one another – they are inter-related in many aspects.

That’s why we often hear and see the people around us using the terms “Artificial Intelligence,” “Machine Learning” and “Deep Learning” interchangeably. However, despite the conceptual similarities, these technologies are unique in their own way. 

Today, we will address one of the less highlighted matters in Data Science – the Deep Learning vs Neural Network debate.

 Before we venture in deep into the Deep Learning vs Neural Network debate, we must understand what these concepts mean individually.

What is Deep Learning?

Deep Learning or Hierarchical Learning is a subset of Machine Learning in Artificial Intelligence that can imitate the data processing function of the human brain and create similar patterns the brain used for decision making. Contrary to task-based algorithms, Deep Learning systems learn from data representations – they can learn from unstructured or unlabeled data.

Deep Learning architectures like deep neural networks, belief networks, and recurrent neural networks, and convolutional neural networks have found applications in the field of computer vision, audio/speech recognition, machine translation, social network filtering, bioinformatics, drug design and so much more. 

What is a Neural Network?

A Neural Networks is made of an assortment of algorithms that are modelled on the human brain. These algorithms can interpret sensory data via machine perception and label or cluster the raw data. They are designed to recognize numerical patterns that are contained in vectors within which all the real-world data (images, sound, text, time series, etc.) has to be translated. 

Essentially, the primary task of a Neural Networks is to cluster and classify the raw data – they group the unlabeled data based on the similarities found in the input data and then classify the data based on the labelled training dataset. Neural Networks can automatically adapt to changing input. So, you need not redesign the output criteria each time the input changes to generate the best possible result. 

Deep Learning vs Neural Network

While Deep Learning incorporates Neural Networks within its architecture, there’s a stark difference between Deep Learning and Neural Networks. Here we’ll shed light on the three major points of difference between Deep Learning and Neural Networks.

1. Definition

Neural Networks – It is a structure consisting of ML algorithms wherein the artificial neurons make the core computational unit that focuses on uncovering the underlying patterns or connections within a dataset, just like the human brain does while decision making.

Deep Learning – It is a branch of Machine Learning that leverages a series of nonlinear processing units comprising multiple layers for feature transformation and extraction. It has several layers of artificial neural networks that carry out the ML process. The first layer of the neural network processes the raw data input and passes the information to the second layer. Deep Learning Career Path: Top Fascinating Job Roles

The second later then processes that information further by adding additional information (for example, user’s IP address) and passes it to the next layer. This process continues throughout all layers of the Deep Learning network until the desired result is achieved.

2. Structure

A Neural Network consists of the following components:

  • Neurons – A neuron is a mathematical function designed to imitate the functioning of a biological neuron. It computes the weighted average of the data input and passes the information through a nonlinear function, a.k.a. The activation function (for examples, the sigmoid). 
  • Connection and weights – As the name suggests, connections connect a neuron in one layer to another neuron in the same layer or another layer. Each connection has a weight value linked to it. Here, a weight represents the strength of the connection between the units. The aim is to reduce the weight value to decrease the possibilities of loss (error).
  • Propagation function – Two propagation functions work in a Neural Network: forward propagation that delivers the “predicted value” and backward propagation that delivers the “error value.”
  • Learning rate – Neural Networks are trained using Gradient Descent to optimize the weights. Back-propagation is used at each iteration to calculate the derivative of the loss function in reference to each weight value and subtract it from that weight. Learning rate decides how quickly or slowly you want to update the weight (parameter) values of the model.

 A Deep Learning model consists of the following components:

  • Motherboard – The motherboard chipset of the model is usually based on PCI-e lanes.
  • Processors – The GPU required for Deep Learning must be determined according to the number of cores and cost of the processor.
  • RAM – This is the physical memory and storage. Since Deep Learning algorithms demand greater CPU usage and storage area, the RAM must be huge.
  • PSU – As the memory demands increase, it becomes crucial to employ a large PSU that can handle massive and complex Deep Learning functions. 

3. Architecture

The architecture of a Neural Network includes:

  • Feed Forward Neural Networks – This is the most common kind of Neural Network architecture wherein the first layer is the input layer, and the final layer is the output layer. All intermediary layers are hidden layers.
  • Recurrent Neural Networks – This network architecture is a series of artificial neural networks wherein the connections between nodes make a directed graph along a temporal sequence. Hence, this type of network depicts temporal dynamic behaviour.
  • Symmetrically Connected Neural Networks – These are similar to recurrent neural networks with the only difference being that in Symmetrically Connected Neural Networks, the connections between units are symmetrical (they have the same weight values in both directions).

The architecture of a Deep Learning model includes:

  • Unsupervised Pre-trained Networks – As the name suggests, this architecture need no formal training since it is pre-trained on past experiences. These include Autoencoders, Deep Belief Networks, and Generative Adversarial Networks.
  • Convolutional Neural Networks – This is a Deep Learning algorithm that can take in an input image, assign importance (learnable weights and biases) to different objects in the image, and also differentiate between those objects. 
  • Recurrent Neural Networks – Recurrent Neural Networks refer to a specific kind of artificial neural network that adds additional weights to the network to create cycles in the network graph so as to maintain an internal state.
  • Recursive Neural Networks – This is a type of Deep Neural Network that is created by applying the same set of weights recursively over a structured input, to produce a structured prediction over or a scalar prediction on variable-size input structures by passing a topological structure.

Conclusion

Since Deep Learning and Neural Networks are so deeply intertwined, it is difficult to tell them apart from each other on the surface level. However, by now, you’ve understood that there’s a significant difference between Deep Learning and Neural Networks.

While Neural Networks use neurons to transmit data in the form of input values and output values through connections, Deep Learning is associated with the transformation and extraction of feature which attempts to establish a relationship between stimuli and associated neural responses present in the brain.

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