EDUCBA

EDUCBA

MENUMENU
  • Explore
    • Lifetime Membership
    • All in One Bundles
    • Fresh Entries
    • Finance
    • Data Science
    • Programming and Dev
    • Excel
    • Marketing
    • HR
    • PDP
    • VFX and Design
    • Project Management
    • Exam Prep
    • All Courses
  • Blog
  • Enterprise
  • Free Courses
  • Login
Home Data Science Data Science Tutorials Head to Head Differences Tutorial Neural Networks vs Deep Learning

Neural Networks vs Deep Learning

Priya Pedamkar
Article byPriya Pedamkar

Updated June 15, 2023

Neural Networks vs Deep Learning

Difference Between Neural Networks vs Deep Learning

With the huge transition in today’s technology, it takes more than just Big Data and Hadoop to transform businesses. Today’s firms are moving towards AI and incorporating machine learning as their new technique. While doing this, they do not have any prior knowledge about the characteristics of cats, but they develop their own set of unique features, which is helpful in their identification. This is based upon learning data representations which are opposite to task-based algorithms. It can further be categorized into supervised, semi-supervised, and unsupervised learning techniques. Let us discuss Neural Networks and Deep Learning in detail in our post.

ADVERTISEMENT
Popular Course in this category
DEEP LEARNING Course Bundle - 40 Courses in 1 | 4 Mock Tests

Start Your Free Data Science Course

Hadoop, Data Science, Statistics & others

Head to Head Comparison Between Neural Networks and Deep Learning (Infographics)

Below is the top 3 Comparison Between Neural Networks and Deep Learning:

Neural Networks vs Deep Learning

Key Differences Between Neural Networks and Deep Learning

  1. Neural networks make use of neurons that are used to transmit data in the form of input values and output values.
  2. Application areas for neural networking include system identification, natural resource management, process control, vehicle control, quantum chemistry, decision making, game playing, face identification, pattern recognition, signal classification, sequence recognition, object recognition, finance, medical diagnosis, visualization, data mining, machine translation, email spam filtering, social network filtering, etc.. In contrast, the application of deep learning includes Automatic speech recognition, image recognition, visual art processing, Natural language processing, drug discovery and toxicology, customer relationship management, recommendation engines, Mobile advertising, bioinformatics, Image restoration, etc.
  3. Criticism encountered for Neural networks includes those training issues, theoretical issues, hardware issues, practical counterexamples to criticisms, and hybrid approaches. In contrast, deep learning, it is related to theory, errors, cyber threats, etc.

Neural Networks and Deep Learning Comparison Table

Below is some key comparison between Neural Networks and Deep Learning.

Basis for comparison Neural Networks Deep Learning
Definition Class of machine learning algorithms where the artificial neuron forms the basic computational unit and networks are used to describe the interconnectivity among each other. It is a class of machine learning algorithms that uses non-linear processing units’ multiple layers for feature transformation and extraction. It also represents concepts in multiple hierarchical fashions corresponding to various levels of abstraction.
Components Neurons: Neuron j receives input from its predecessor neurons, typically in the form of an identity function, to generate an output.
Connections and weights: The connection is a vital component between the output neuron i and the input neuron j.
Propagation function: It provides an input for the resulting output.
Learning rule: It is used to modify the parameters of a neural network so as to result in a favorable output.
Motherboard: The motherboard chipset is a component related to deep learning, which is particularly based on PCI-e lanes.
RAM, physical memory, and storage: The deep learning algorithms require great CPU usage, storage, and memory area, and so having a rich set of these components is a must.
PSU: With the increase in memory, CPU, and storage area, it also becomes important to use a large PSU enough to handle huge power.
Architecture Feed Forward Neural Networks: The commonest kind of architecture contains the first layer as the input layer. In contrast, the last layer is the output layer, and all the intermediary layers are the hidden layers.
Recurrent networks: This kind of architecture consists of directed cycles in the connection graph. The biologically realistic architectures can also take you back from where you started.
Symmetrically connected networks: Symmetrical connection holding architecture which is more or less like the recurrent networks. On the other hand, Hopfield nets, which do not have a hidden layer, are another type of neural network.
Unsupervised Pretrained Networks: In this architecture, we discuss no formal training, but the networks are pretrained using past experiences. This includes autoencoders, deep belief networks, and generative adversarial networks.
Convolutional Neural Networks: It aims to learn higher-order features using convolutions which betters the image recognition and identification user experience. Identification of faces, street signs, platypuses, and other objects becomes easy using this architecture.
Recurrent neural networks: They come from the family of feedforward, which beliefs in sending their information over time steps.
Recursive neural networks: It also marks variable length input. The primary difference between recurrent and recursive is that the former can devise the hierarchical structures in the training dataset.

Conclusion

AI is an extremely powerful and interesting field that only will become more ubiquitous and important moving forward and will surely have huge impacts on society as a whole. These two techniques are some of AI’s very powerful tools to solve complex problems and will continue to develop and grow in the future for us to leverage them.

Recommended Articles

This has been a guide to Neural Networks vs Deep Learning. Here we have discussed Neural Networks vs Deep Learning head-to-head comparison, key differences along with infographics and comparison table. You may also look at the following articles to learn more –

  1. Best 7 Difference Between Data Mining Vs Data Analysis
  2. Machine Learning vs Predictive Analytics – 7 Useful Differences
  3. Data Mining Vs Data Visualization – Which One Is Better
  4. Business Intelligence vs BigData – 6 Amazing Comparisons
ADVERTISEMENT
EVIEWS Course Bundle - 11 Courses in 1
22+ Hours of HD Videos
11 Courses
Verifiable Certificate of Completion
Lifetime Access
4.5
ADVERTISEMENT
MYSQL Course Bundle - 18 Courses in 1 | 3 Mock Tests
93+ Hour of HD Videos
18 Courses
3 Mock Tests & Quizzes
Verifiable Certificate of Completion
Lifetime Access
4.5
ADVERTISEMENT
CLOUD COMPUTING Course Bundle - 23 Courses in 1
97+ Hours of HD Videos
23 Courses
Verifiable Certificate of Completion
Lifetime Access
4.5
ADVERTISEMENT
SPLUNK Course Bundle - 12 Courses in 1
55+ Hours of HD Videos
12 Courses
Verifiable Certificate of Completion
Lifetime Access
4.5
Primary Sidebar
Footer
About Us
  • Blog
  • Who is EDUCBA?
  • Sign Up
  • Live Classes
  • Certificate from Top Institutions
  • Contact Us
  • Verifiable Certificate
  • Reviews
  • Terms and Conditions
  • Privacy Policy
  •  
Apps
  • iPhone & iPad
  • Android
Resources
  • Free Courses
  • Database Management
  • Machine Learning
  • All Tutorials
Certification Courses
  • All Courses
  • Data Science Course - All in One Bundle
  • Machine Learning Course
  • Hadoop Certification Training
  • Cloud Computing Training Course
  • R Programming Course
  • AWS Training Course
  • SAS Training Course

ISO 10004:2018 & ISO 9001:2015 Certified

© 2023 - EDUCBA. ALL RIGHTS RESERVED. THE CERTIFICATION NAMES ARE THE TRADEMARKS OF THEIR RESPECTIVE OWNERS.

EDUCBA

*Please provide your correct email id. Login details for this Free course will be emailed to you

Let’s Get Started

By signing up, you agree to our Terms of Use and Privacy Policy.

EDUCBA
Free Data Science Course

Hadoop, Data Science, Statistics & others

By continuing above step, you agree to our Terms of Use and Privacy Policy.
*Please provide your correct email id. Login details for this Free course will be emailed to you

EDUCBA

*Please provide your correct email id. Login details for this Free course will be emailed to you
EDUCBA

*Please provide your correct email id. Login details for this Free course will be emailed to you
EDUCBA Login

Forgot Password?

By signing up, you agree to our Terms of Use and Privacy Policy.

This website or its third-party tools use cookies, which are necessary to its functioning and required to achieve the purposes illustrated in the cookie policy. By closing this banner, scrolling this page, clicking a link or continuing to browse otherwise, you agree to our Privacy Policy

Loading . . .
Quiz
Question:

Answer:

Quiz Result
Total QuestionsCorrect AnswersWrong AnswersPercentage

Explore 1000+ varieties of Mock tests View more

🚀 Extended Cyber Monday Price Drop! All in One Universal Bundle (3700+ Courses) @ 🎁 90% OFF - Ends in ENROLL NOW