Difference Between Neural Network and Deep Learning

The key difference between neural network and deep learning is that neural network operates similar to neurons in the human brain to perform various computation tasks faster while deep learning is a special type of machine learning that imitates the learning approach humans use to gain knowledge.

Neural network helps to build predictive models to solve complex problems. On the other hand, deep learning is a part of machine learning. It helps to develop speech recognition, image recognition, natural language processing, recommendation systems, bioinformatics and many more. Neural Network is a method to implement deep learning.

CONTENTS

1. Overview and Key Difference
2. What is Neural Network
3. What is Deep Learning
4. Side by Side Comparison – Neural Network vs Deep Learning in Tabular Form
5. Summary

What is Neural Network?

Biological neurons are the inspiration for neural networks. There are millions of neurons in the human brain and information process from one neuron to another. Neural Networks use this scenario. They create a computer model similar to a brain. It can perform computational complex tasks faster than a usual system.

In a neural network, the nodes connect to each other. Each connection has a weight. When the inputs to the nodes are x1, x2, x3,… and the corresponding weights are w1, w2, w3,… then the net input (y) is,

 y = x1w1 + x2w2 + x3w3 + ….

After applying the net input to the activation function, it gives the output. The activation function can be linear or sigmoid function.

Y = F(y)

If this output is different from desired output, the weight is adjusted again and this process continuous till getting the desired output. This updating weight happens according to backpropagation algorithm.

There are two neural network topologies called feedforward and feedback. The feedforward networks have no feedback loop. In other words, the signals only flow from input to the output. Feedforward networks further divide to a single layer and multi-layer neural networks.

Network Types

In single layer networks, the input layer connects to the output layer. Multi-layer neural network has more layers between the input layer and the output layer. Those layers are called the hidden layers. The other network type which is the feedback networks have feedback paths. Moreover, there is a possibility to pass information to both sides.

A neural network learns by modifying the weights of the connection between the nodes. There are three learning types, such as supervised learning, unsupervised learning and reinforcement learning. In supervised learning, the network will provide an output vector according to the input vector. This output vector is compared with the desired output vector. If there is a difference, the weights will modify. This processes continue until the actual output matches with the desired output.

In unsupervised learning, the network identifies the patterns and features from input data and relation for input data by itself. In this learning, input vectors of similar types combine to create clusters. When the network gets a new input pattern, it will give the output specifying the class to which that input pattern belongs to. The reinforcement learning accepts some feedback from the environment. Then the network changes the weights.  Those are the methods to train a neural network. Overall, neural networks help to solve various pattern recognition problems.

What is Deep Learning?

Before deep learning, it is important to discuss machine learning. It gives the ability for a computer to learn without explicitly programmed. In other words, it helps to create self-learning algorithms to analyse data and recognise patterns to make decisions. But, there are some limitations is general machine learning. Firstly, it is difficult to work with high dimensional data or extremely large set of inputs and outputs. It might be also difficult to do feature extraction.

Deep learning solves these issues. It is a special type of machine learning. It helps to build learning algorithms that can function similar to human brain. Deep neural networks and recurrent neural networks are some deep learning architectures. A deep neural network is a Neural network with multiple hidden layers. Recurrent neural networks use memory to process sequences of inputs.

What is the Difference Between Neural Network and Deep Learning?

A Neural Network is a system that operates similar to neurons in the human brain to perform various computation tasks faster. Deep learning is a special type of machine learning that imitates the learning approach humans use to gain knowledge. Neural Network is a method of achieving deep learning. On the other hand, Deep Leaning is a special form of Machine Leaning. This is the main difference between neural network and deep learning

Summary – Neural Network vs Deep Learning

The difference between neural network and deep learning is that neural network operates similar to neurons in the human brain to perform various computation tasks faster while deep learning is a special type of machine learning that imitates the learning approach humans use to gain knowledge.

Reference:

1.“What Is Deep Learning (Deep Neural Network)? – Definition from WhatIs.com.” SearchEnterpriseAI. Available here 
2.“Deep Learning.” Wikipedia, Wikimedia Foundation, 30 May 2018. Available here  
3.edurekaIN. What Is Deep Learning | Deep Learning Simplified | Deep Learning Tutorial | Edureka, Edureka!, 10 May 2017. Available here   
4.Tutorials Point. “Artificial Neural Network Building Blocks.”  Tutorials Point, 8 Jan. 2018. Available here  

Image Courtesy:

1.’Artificial neural network’By Geetika saini – Own work, (CC BY-SA 4.0) via Commons Wikimedia  
2.’MultiLayerNeuralNetworkBigger english’By MultiLayerNeuralNetwork_english.png: Chrislbderivative work: —  HELLKNOWZ  ▎TALK  ▎enWP TALK (CC BY-SA 3.0) via Commons Wikimedia