EDUCBA

EDUCBA

MENUMENU
  • Free Tutorials
  • Free Courses
  • Certification Courses
  • 360+ Courses All in One Bundle
  • Login
Home Data Science Data Science Tutorials Machine Learning Tutorial Deep Learning Networks
Secondary Sidebar
Machine Learning Tutorial
  • Deep Learning
    • What Is Deep learning
    • Overviews Deep Learning
    • Application of Deep Learning
    • Careers in Deep Learnings
    • Deep Learning Frameworks
    • Deep Learning Model
    • Deep Learning Algorithms
    • Deep Learning Technique
    • Deep Learning Networks
    • Deep Learning Libraries
    • Deep Learning Toolbox
    • Types of Neural Networks
    • Convolutional Neural Networks
    • Create Decision Tree
    • Deep Learning for NLP
    • Caffe Deep Learning
    • Deep Learning with TensorFlow
  • Basic
    • Introduction To Machine Learning
    • What is Machine Learning?
    • Uses of Machine Learning
    • Applications of Machine Learning
    • Naive Bayes in Machine Learning
    • Dataset Labelling
    • DataSet Example
    • Deep Learning Techniques
    • Dataset ZFS
    • Careers in Machine Learning
    • What is Machine Cycle?
    • Machine Learning Feature
    • Machine Learning Programming Languages
    • What is Kernel in Machine Learning
    • Machine Learning Tools
    • Machine Learning Models
    • Machine Learning Platform
    • Machine Learning Libraries
    • Machine Learning Life Cycle
    • Machine Learning System
    • Machine Learning Datasets
    • Machine Learning Certifications
    • Machine Learning Python vs R
    • Optimization for Machine Learning
    • Types of Machine Learning
    • Machine Learning Methods
    • Machine Learning Software
    • Machine Learning Techniques
    • Machine Learning Feature Selection
    • Ensemble Methods in Machine Learning
    • Support Vector Machine in Machine Learning
    • Decision Making Techniques
    • Restricted Boltzmann Machine
    • Regularization Machine Learning
    • What is Regression?
    • What is Linear Regression?
    • Dataset for Linear Regression
    • Decision tree limitations
    • What is Decision Tree?
    • What is Random Forest
  • Algorithms
    • Machine Learning Algorithms
    • Apriori Algorithm in Machine Learning
    • Types of Machine Learning Algorithms
    • Bayes Theorem
    • AdaBoost Algorithm
    • Classification Algorithms
    • Clustering Algorithm
    • Gradient Boosting Algorithm
    • Mean Shift Algorithm
    • Hierarchical Clustering Algorithm
    • Hierarchical Clustering Agglomerative
    • What is a Greedy Algorithm?
    • What is Genetic Algorithm?
    • Random Forest Algorithm
    • Nearest Neighbors Algorithm
    • Weak Law of Large Numbers
    • Ray Tracing Algorithm
    • SVM Algorithm
    • Naive Bayes Algorithm
    • Neural Network Algorithms
    • Boosting Algorithm
    • XGBoost Algorithm
    • Pattern Searching
    • Loss Functions in Machine Learning
    • Decision Tree in Machine Learning
    • Hyperparameter Machine Learning
    • Unsupervised Machine Learning
    • K- Means Clustering Algorithm
    • KNN Algorithm
    • Monty Hall Problem
  • Supervised
    • What is Supervised Learning
    • Supervised Machine Learning
    • Supervised Machine Learning Algorithms
    • Perceptron Learning Algorithm
    • Simple Linear Regression
    • Polynomial Regression
    • Multivariate Regression
    • Regression in Machine Learning
    • Hierarchical Clustering Analysis
    • Linear Regression Analysis
    • Support Vector Regression
    • Multiple Linear Regression
    • Linear Algebra in Machine Learning
    • Statistics for Machine Learning
    • What is Regression Analysis?
    • Clustering Methods
    • Backward Elimination
    • Ensemble Techniques
    • Bagging and Boosting
    • Linear Regression Modeling
    • What is Reinforcement Learning
  • Classification
    • Kernel Methods in Machine Learning
    • Clustering in Machine Learning
    • Machine Learning Architecture
    • Automation Anywhere Architecture
    • Machine Learning C++ Library
    • Machine Learning Frameworks
    • Data Preprocessing in Machine Learning
    • Data Science Machine Learning
    • Classification of Neural Network
    • Neural Network Machine Learning
    • What is Convolutional Neural Network?
    • Single Layer Neural Network
    • Kernel Methods
    • Forward and Backward Chaining
    • Forward Chaining
    • Backward Chaining
  • RPA
    • What is RPA
    • What is Robotics?
    • Benefits of RPA
    • RPA Applications
    • Types of Robots
    • RPA Tools
    • Line Follower Robot
    • What is Blue Prism?
    • RPA vs BPM
  • Interview Questions
    • Deep Learning Interview Questions And Answer
    • Machine Learning Cheat Sheet

Related Courses

Machine Learning Training

Deep Learning Training

Artificial Intelligence Training

Deep Learning Networks

By Priya PedamkarPriya Pedamkar

Deep Learning Networks

Introduction to Deep Learning Networks

Deep Learning networks are the mathematical models that are used to mimic the human brains as it is meant to solve the problems using unstructured data, these mathematical models are created in form of neural network that consists of neurons. The neural network is divided into three major layers that are input layer( first layer of neural network), hidden layer (all the middle layer of neural network) and the output layer(last layer of the neural network.). Based upon these types of data we will deal with these neural networks that are classified as a feed-forward neural network, CNN, RNN, Modular neural network, etc.

Working of Deep Learning Networks

Let’s see the working of the network in general.

Working of Deep Learning Networks

Consider the above neural network which will help predict the image of digits

Start Your Free Data Science Course

Hadoop, Data Science, Statistics & others

  • There is an image of number ‘9’ which is 28 x 28 pixels.
  • All of the pixel that is 28×28 = 784 pixels are fed into the input layer
  • All those neurons have a value called an activation function so when evert that number is met that particular neuron will get fired and the value will pass to the next layer that is the hidden layer
  • The same procedure will follow for both the hidden layers.
  • After the hidden layer’s operation is done the control will go to the output layer this control flow that is from the input layer to the output layer is called forward propagation
  • Once the output is generated in the output layer it will get matched with the actual output of the number ‘9’ and the deviation between the predicted and the actual output will be calculated that is known as the loss function.
  • Once the loss is calculated that the same information is passed back from the output layer to the input layer via those hidden layers. This phenomenon is called the backpropagation
  • And then the inputs are adjusted accordingly and the network gets trained

Types of Deep Learning Networks

Now let’s see what are the different types of deep learning networks available

All in One Data Science Bundle(360+ Courses, 50+ projects)
Python TutorialMachine LearningAWSArtificial Intelligence
TableauR ProgrammingPowerBIDeep Learning
Price
View Courses
360+ Online Courses | 50+ projects | 1500+ Hours | Verifiable Certificates | Lifetime Access
4.7 (86,584 ratings)

1. Feedforward neural network

Feedforward neural network

  • This type of neural network is the very basic neural network where the flow control occurs from the input layer and goes towards the output layer.
  • These kinds of networks are only having single layers or only 1 hidden layer
  • Since the data moves only in 1 direction there is no backpropagation technique in this network
  • In this network, the sum of the weights present in the input is fed into the input layer
  • These kinds of networks are used in the facial recognition algorithm using computer vision.

2. Radial basis function neural networks

  • This kind of neural network have generally more than 1 layer preferably two layers
  • In this kind of network, the relative distance from any point to the center is calculated and the same is passed towards the next layer
  • Radial basis networks are generally used in power restoration systems to restore the power in the shortest span of time to avoid blackouts.

3. Multi-layer perceptron

Multi-layer perceptron

  • This type of network are having more than 3 layers and its used to classify the data which is not linear
  • These kinds of networks are fully connected with every node.
  • These networks are extensively used for speech recognition and other machine learning technologies.

4. Convolution neural network (CNN)

 Convolution neural network

  • CNN is one of the variations of the multilayer perceptron.
  • CNN can contain more than 1 convolution layer and since it contains a convolution layer the network is very deep with fewer parameters.
  • CNN is very effective for image recognition and identifying different image patterns.

5. Recurrent neural network

Recurrent neural network

  • RNN is a type of neural network where the output of a particular neuron is fed back as an input to the same node.
  • This method helps the network to predict the output.
  • This kind of network is useful in maintaining a small state of memory which is very useful for developing the chatbot
  • This kind of network is used in chatbot development and text-to-speech technologies.

6. Modular neural network

Modular neural network

  • This kind of network is not a single network but a combination of multiple small neural networks.
  • All the sub-networks make a big neural network and all of them work independently to achieve a common target.
  • These networks are very helpful in breaking the small-large problem into small pieces and then solving it.

7. Sequence to sequence models

  • This type of network is generally a combination of two RNN networks.
  • The network works on the encoding and decoding that is it consists of the encoder which is used to process the input and there is a decoder which processes the output
  • Generally, this kind of network is used for text processing where the length of the inputted text is not as same as outputted text.

Conclusion

In this article, we have seen what is meant by deep learning and what are all the different deep learning networks currently used in the market. We have also seen the intricacies of the working of all those networks and the application of those networks.

Recommended Articles

This is a guide to Deep Learning Networks. Here we discuss the working of deep learning networks along with 7 different types in detail. You may also have a look at the following articles to learn more –

  1. Deep Learning Technique
  2. Deep Learning Algorithms
  3. Careers in Deep Learnings
  4. Deep Learning Libraries
Popular Course in this category
Deep Learning Training (18 Courses, 24+ Projects)
  18 Online Courses |  24 Hands-on Projects |  145+ Hours |  Verifiable Certificate of Completion
4.5
Price

View Course

Related Courses

Machine Learning Training (20 Courses, 29+ Projects)4.9
Artificial Intelligence AI Training (5 Courses, 2 Project)4.8
1 Shares
Share
Tweet
Share
Primary Sidebar
Footer
About Us
  • Blog
  • Who is EDUCBA?
  • Sign Up
  • Live Classes
  • Corporate Training
  • 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

© 2022 - EDUCBA. ALL RIGHTS RESERVED. THE CERTIFICATION NAMES ARE THE TRADEMARKS OF THEIR RESPECTIVE OWNERS.

EDUCBA
Free Data Science Course

SPSS, Data visualization with Python, Matplotlib Library, Seaborn Package

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

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

EDUCBA Login

Forgot Password?

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

EDUCBA
Free Data Science Course

Hadoop, Data Science, Statistics & others

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

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

EDUCBA

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

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

Let’s Get Started

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