Distinguishing between Deep Learning and Neural Networks in Machine Learning!

In recent years the advancement of Artificial Intelligence technology has made people familiar with the terms Machine Learning, Deep learning, and Neural networks. There are numerous applications of Deep Learning and Neural Networks in Machine learning.

Deep learning and Neural networks analyze complex datasets and accomplish high accuracy in tasks that classical algorithms find challenging. These are most suitable for handling unstructured and unlabeled data. Most people assume that terms like deep learning, neural networks, and machine learning are similar because of their deeply interconnected nature. However, Deep learning and Neural networks in machine learning are unique and perform different useful functions.

Deep learning and Neural Networks are sub-branch of machine learning that play a prominent role in developing machine learning algorithms that automate human activities. In this article, you will learn about Deep Learning and Neural Networks in Machine learning.

Neural Networks in Machine Learning:-

Neural networks are designed to imitate the human brain using machine learning algorithms. A neural network works the way biological neurons work; neural network units in artificial intelligence are called Artificial Neurons.

Artificial Neural Network(ANN) comprises three interconnected layers: the input layer, the hidden layer, and the output layer. The input layer receives the raw data and processes it, then it is passed on to hidden layers, and at the end, the processed output data reaches the output layer.

The neural network algorithms cluster, classify and label the data through machine perception. They are mainly designed to identify numerical patterns in vector data that can be converted into real-world data like images, audio, texts, time series, etc.

Deep Learning in Machine Learning:-

Deep learning is a subset of machine learning designed to imitate how a human brain processes data. It creates patterns similar to the human brain that helps in decision-making. Deep learning can learn from structures and unstructured data in a hierarchical manner.

Deep learning consists of multiple hidden layers of nodes called Deep neural networks or Deep Learning systems. Deep neural networks are used to train with complex data and predict based on data patterns. Convolutional Neural Networks, Recurrent Neural Networks, Deep neural networks, and Belief Networks are some examples of deep learning in machine learning architecture.

Differences between Deep Learning and Neural Networks in Machine Learning

PARAMETER

DEEP LEARNING

NEURAL NETWORK

Definition

It is a machine learning architecture consisting of multiple artificial neural networks (hidden layers) for featured extraction and transformation.

It is an ML structure comprising computational units called Artificial Neurons designed to mimic the human brain.

Structure

The components of deep learning include:-

Motherboard

Processors

Large RAM unit

PSU

The components of the neural network include:-

Neuron

Connection & Weights

Propagation Function

Learning rate

PARAMETER

DEEP LEARNING

NEURAL NETWORK

Architecture

The deep learning model architecture consists of 3 types:-

Unsupervised Pre-trained Neural Networks

Convolutional Neural Networks

Recurrent Neural Networks

The neural network model architecture consists of:-

Feedforward Neural Networks

Recurrent Neural Networks

Symmetrically Connected Neural Networks

Time & Accuracy

It takes more time to train deep learning models, but they achieve high accuracy.

It takes less time to train neural networks and features a low accuracy rate.

Performance

Deep learning models perform tasks faster and more efficiently than neural networks

Neural Networks perform poorly compared to deep learning.

Applications

Various applications of Deep Learning:-

Image recognition

Speech recognition

Visual art processing

Bioinformatics

Recommendation Engines

Various applications of Neural Networks:-

Vehicle control

Quantum Chemistry

Pattern recognition

Natural resource management

Machine translation

Summing Up

Deep learning and neural networks are popular algorithms in machine learning architecture because of their ability to perform different tasks efficiently. On a surface level, deep learning and neural networks seem similar, and now we have seen the differences between these two in this blog.

Deep learning and Neural networks have complex architectures to learn. To distinguish more about deep learning and neural network in machine learning, one must learn more about machine learning algorithms. If you are confused about how to learn about machine learning algorithms, you should check out Advanced Artificial Intelligence and Machine Learning for in-depth learning.