Figure above shows the differences between Artificial Intelligence, Machine Learning, Neural Networks, and Deep Learning. In this chapter, we will discuss about Deep Learning, while, there is huge similarity with Neural Network (I have written the course materials about it you can check it). The inspiration from these inovation has been inspired by the human brain. Then, now, we can differentiate between both of Neural Network and Deep Learning by the following explanations.

Difference between a Neural Network and a Deep Learning System

Artificial intelligence and machine learning have come a long way since their inception in the late 1950s. These technologies have become increasingly complex and advanced in recent years. While technological advancements in the Data Science domain are commendable, they have resulted in a flood of terminologies that are beyond the average person's comprehension. However, it is quite difficult to distinguish between Neural Network and Deep Learning.

Neural Network

After my explanation in Neural Network, here is the summary of the simple explanation about Neural Networks:

From the figure above, we can summarize Neural Networks for 4 definitions:

# The most complex object in the universe – the human brain – inspired neural networks. First, let us examine how the brain functions. Neurons are the building blocks of the human brain. The most fundamental computational unit of any neural network, including the brain, is the neuron.

# Neurons receive input, process it, and pass it on to other neurons in the network's multiple hidden layers until the processed output reaches the Output Layer.

# Neural networks are algorithms that can understand sensory input and label or categorize it using machine perception. They are intended to identify numerical patterns included in vectors that must be transformed in order to change all real-world data (images, sounds, text, time series, etc.)

# An Artificial Neural Network (ANN) contains only three layers in its most basic form: an input layer, an output layer, and a hidden layer.

Deep Learning (Deep Neural Network)

Now that we've discussed Neural Networks, let's move on to Deep Learning. Deep learning, also known as hierarchical learning, is a subset of machine learning in artificial intelligence that may replicate the processing capabilities of the human brain and establish patterns similar to those utilized by the brain to make choices. Deep learning systems, in contrast to task-based algorithms, learn from data representations. It can learn from unstructured or unlabeled data.

The figure above explained Deep Learning, we can see the total of each layer is more complex than Neural Networks. There are two explanations that we can point out:

# A deep learning system or a deep neural network is a neural network with several hidden layers and many nodes in each hidden layer. The development of deep learning algorithms that can be used to train and anticipate output from complicated data is known as deep learning.

# The term "deep" in Deep Learning refers to the number of hidden layers, or the neural network's depth. A Deep Learning Model is essentially any neural network with more than three layers, including the Input Layer and Output Layer.

Differences between a Neural Network and a Deep Learning System are listed in the table below.

  • Differences Neural Networks Deep Learning
    Definition A neural network is a network of neurons modeled after the human brain. It is composed of several neurons that are interconnected with one another. The depth or number of hidden layers distinguishes deep learning neural networks from other types of neural networks.
    Architecture • Feed Forward Neural Networks
    • Recurrent Neural Networks
    • Symmetrically Connected Neural Networks
    • Recursive Neural Networks
    • Unsupervised Pre-trained Networks
    • Convolutional Neural Networks
    Structure • Neurons
    • Connection and weights
    • Propagation function
    • Learning rate
    • Motherboards
    • PSU
    • RAM
    • Processors
    Time & Accuracy • It generally takes less time to train them.
    • They have a lower accuracy than Deep Learning Systems
    • It generally takes more time to train them.
    • They have a higher accuracy than Deep Learning Systems
  • Then, now we can see the differences in detailed :


    Neural Network

    • Feedforward Neural Networks — The most prevalent sort of neural network design, with the first layer serving as the input layer and the last as the output layer. All of the intermediate levels are concealed.

    • Recurrent Neural Network - This network design is made up of a succession of ANNs in which node connections create a directed graph along a temporal sequence. As a result, this network shows dynamic behavior throughout time.

    • Symmetrically Connected Neural Networks — These are identical to recurrent neural networks, with the exception that the connections between units in symmetrically connected neural networks are symmetric (i.e. same weight n both directions).

    Deep Learning

    • Unsupervised Pre-Trained Network — As the name suggests, this architecture is pre-trained on previous experiences and does not require formal training. These are some examples: Deep Belief Networks with Autoencoders

    • Convolutional Neural Network — This is a deep learning technique that can take an input image and give meaning to various things in the image (learnable weights and biases), as well as discriminate between these items.

    • Recursive Neural Network – This is formed by applying the same set of weights to a structured input iteratively and passing a topological structure to construct a structured prediction over a scalar prediction on a variable-size input structure.


    Neural Network

    • Neurons are mathematical functions that attempt to mimic the behavior of biological neurons. It computes the weighted average of the data supplied before passing it through a nonlinear function known as the logistic function.

    • Connections and weights – As the name implies, connections connect a neuron in one layer to another neuron in the same or another layer. Each connection is given a weight value. A weight represents the strength of the relationship between the units. The goal is to reduce the weight number in order to reduce the likelihood of losing weight (error).

    • Propagation – There are two propagation functions in a Neural Network: forward propagation, which produces the "predicted value," and backward propagation, which produces the "error value."

    • To train neural networks, Learning Rate – Gradient Descent is used. The derivative of the loss function is calculated in reference to each weight value using back-propagation and then subtracted from that weight at each iteration. The learning rate determines how quickly or slowly the model's weight values are updated.

    Deep Learning

    • Motherboard – The deep learning model's motherboard chips are typically based on PCI-e lanes.

    • Processors – The GPU requirements for Deep Learning models must be determined based on the number of cores and cost of the processor.

    • RAM – Deep learning models necessitate massive amounts of computational power and storage. This necessitates the use of larger RAMs.

    • Power Supply Unit (PSU) – As memory requirements increase, it becomes increasingly important to have a large Power Supply Unit capable of handling massive and complex Deep Learning functions.


    Because Deep Learning and Neural Networks are so closely related, it can be difficult to distinguish between them on the surface. However, as you've probably guessed, Deep Learning and Neural Networks are not the same thing. Deep Learning is associated with the transformation and extraction of features that attempt to establish a relationship between stimuli and associated neural responses present in the brain, whereas Neural Networks use neurons to transmit data in the form of input to obtain output using various connections.