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Deep Learning vs Neural Networks

Data science has become a part of our lives directly and indirectly. Its wide range of applications in our daily lives includes intelligent devices like Amazon’s Alexa, Siri, meteorological forecasting, customized advertisements, chatbots, recommendations on different websites, and so much more. It is really fascinating how we all have shifted to this era of data. It has revolutionized every industry and organization in some way. And, it continues to do so by adding new functionalities and providing better outputs and customer satisfaction.

Deep Learning

The multi-dimensional approach to data science has made life easier on so many levels. Nonetheless, it is not fully explored and offers a great scope of research and development. No doubt there is a high rise in job opportunities in his domain. Terms like Machine Learning, Artificial Intelligence, Neural Networks, and Deep Learning are often used interchangeably and lead to confusion among common masses. Here, we address two of these technologies, deep learning, and neural networks. Let us consider them individually.

Difference between Deep Learning and Neural Network

Neural Networks are incorporated into the architecture of Deep Learning. However, the two are a lot different than each other. In this article, we will be discussing the three major differences between Neural Networks and Deep Learning:

1.    Definition

Neural Networks – The neural network is composed of a compilation of algorithms that mimics the human brain. Human brains are made up of interconnected neurons with different parts performing different functions. The different parts of the brain are arranged in a hierarchical order. The information comes to the lowest level, and the related functionality is performed and then the outcome is passed on to the higher levels. Artificial neural networks try to simulate this functionality through a hierarchical approach.

Deep Learning – It is the subdomain of Machine Learning imitating the human brain’s data processing function and learning from unlabeled or unstructured data. The computer trains itself to process and learns from data, unlike machine learning which teaches computers to learn from data. An Artificial Neural Network or ANN is made up of three layers, an input layer, an output layer, and multiple hidden layers, which is termed as a deep neural network.

2.    Structure

Neural Network – Every neural network consists of at least two components – the processing elements and their connection. These processing elements are known as neurons while their connection is called links. With each link, there is an associated weight parameter. Each neuron gets a stimulus from its neighboring neuron. It processes the information and then produces an output. Each neuron receives stimuli from outside of the network known as input neurons. Output neurons are the ones whose outputs are externally used. In between lies the hidden neurons that receive stimulus from other neurons and outputs stimuli for other neurons. A neuron can process this information in multiple ways and connect to other neurons in different ways as well. The construction of different neural structures can be done through different processing elements.

A wide range of neural network structures has been designed for pattern recognition, signal processing, control, and others. A few examples of these neural network structures include Multilayer Perceptrons (MLP), Self-Organizing Maps (SOM), Radial-Basis Function Networks (RBF), Recurrent Networks, Arbitrary Structures, and Wavelet Neural Networks.

Deep Learning – In a Deep Learning model, every layer of the node is trained on a set of features based on the output of the last layer. As you advance into the network, your nodes will be able to recognize more complex features. This is feature Hierarchy where each layer is aggregating and recombining the features from the previous layer. With this, a deep-learning network is able to handle large, high-dimensional datasets as the non-linear functions can take on billions of parameters. Deep Learning networks can also be used for Automatic Feature Extraction without any human intervention.

3.    Architecture

Neural Network – Neural Networks are what helps human process information. These are formed with billions and trillions of neurons that exchange brief electrical pulses. Artificial Neural Networks are the computer algorithms mimicking these biological structures. A Neural Network structure has three layers – input, hidden, and output. Each layer has multiple nodes that allow the flow of information. Some neural networks’ structure includes intricate connections like feedback paths. The input layer’s nodes are passive which means that they don’t modify data. A single value is received as input and duplicate value for multiple outputs. The hidden and output layers’ nodes are active.

In most applications, the network will be of a three-layer structure with about a couple hundred input nodes. Usually, the hidden layer is 10% of the input layer’s size. For target detection, only a single node is needed by the output layer. The output value will be thresholded for providing an indication, positive or negative, of the input data’s presence or absence.

Deep Learning – There are different architectures used in deep learning, including the following:

  1. Convolutional Neural Networks (CNN) – It is a common choice used for Computer Vision tasks like Image Recognition. There are 4 primary steps involved in CNN design:
  • Convolution – This stage receives the input signal.
  • Subsampling – This involves smoothening the inputs received from convolution layers for reducing the filters’ sensitivity to variation like noise.
  • Activation – This layer is for controlling the flow of signals from one layer to another.
  • Fully connected – This is the last stage where all the network layers are connected using every neuron from the last layer to the neurons from the next layer.
  • Recurrent Neural Network (RNN) – In this network, every single element performs a unified task with the output that depends on the previous computations. These networks have a memory that stores every calculated information and utilize it for calculating the final outcome. Here are a few varieties of RNN:
  • Bidirectional RNN – In this RNN, the output depends on the past as well as future outcomes.
  • Deep RNN – This type of RNN includes multiple layers at every step that allows better learning and accuracy.
  • Autoencoders – Autoencoders involve applying the backpropagation principle in the unsupervised environment. It is similar to the Principle Component Analysis (PCA) but is more flexible. The four major forms of Autoencoders are:
  • Vanilla autoencoder – It is a simple form of autoencoder with just one hidden layer.
  • Multilayer autoencoder – In this, the autoencoder is extended to several hidden layers.
  • Convolutional autoencoder – In this, fully-connected layers are replaced with convolutions.
  • Regularized autoencoder – This form of the network involves using a special loss function. It gives properties to the model that is more than just copying input to output.
  • Generative Adversarial Networks – The premise of GANs is simultaneously training two deep learning models. Both networks are competing with each other. One model known as the Generator creates new examples or instances while the other model, Discriminator, classifies if the instance is from the Generator or the training data.
  • ResNets – This type of network includes several residual modules. Each module in the network is representing a layer. There is a certain set of functions that must be performed on every layer’s input.

There is no denying that Deep Learning and Neural Networks are intertwined. On the surface level, it might be difficult to differentiate between the two. However, now, you must have understood that both of them differ significantly. If you want to know more about Deep Learning, you should enrol in a Deep Learning course that will help you get an in-depth understanding of the concept.

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