- Blog Categories
- Software Development Projects and Ideas
- 12 Computer Science Project Ideas
- 28 Beginner Software Projects
- Top 10 Engineering Project Ideas
- Top 10 Easy Final Year Projects
- Top 10 Mini Projects for Engineers
- 25 Best Django Project Ideas
- Top 20 MERN Stack Project Ideas
- Top 12 Real Time Projects
- Top 6 Major CSE Projects
- 12 Robotics Projects for All Levels
- Java Programming Concepts
- Abstract Class in Java and Methods
- Constructor Overloading in Java
- StringBuffer vs StringBuilder
- Java Identifiers: Syntax & Examples
- Types of Variables in Java Explained
- Composition in Java: Examples
- Append in Java: Implementation
- Loose Coupling vs Tight Coupling
- Integrity Constraints in DBMS
- Different Types of Operators Explained
- Career and Interview Preparation in IT
- Top 14 IT Courses for Jobs
- Top 20 Highest Paying Languages
- 23 Top CS Interview Q&A
- Best IT Jobs without Coding
- Software Engineer Salary in India
- 44 Agile Methodology Interview Q&A
- 10 Software Engineering Challenges
- Top 15 Tech's Daily Life Impact
- 10 Best Backends for React
- Cloud Computing Reference Models
- Web Development and Security
- Find Installed NPM Version
- Install Specific NPM Package Version
- Make API Calls in Angular
- Install Bootstrap in Angular
- Use Axios in React: Guide
- StrictMode in React: Usage
- 75 Cyber Security Research Topics
- Top 7 Languages for Ethical Hacking
- Top 20 Docker Commands
- Advantages of OOP
- Data Science Projects and Applications
- 42 Python Project Ideas for Beginners
- 13 Data Science Project Ideas
- 13 Data Structure Project Ideas
- 12 Real-World Python Applications
- Python Banking Project
- Data Science Course Eligibility
- Association Rule Mining Overview
- Cluster Analysis in Data Mining
- Classification in Data Mining
- KDD Process in Data Mining
- Data Structures and Algorithms
- Binary Tree Types Explained
- Binary Search Algorithm
- Sorting in Data Structure
- Binary Tree in Data Structure
- Binary Tree vs Binary Search Tree
- Recursion in Data Structure
- Data Structure Search Methods: Explained
- Binary Tree Interview Q&A
- Linear vs Binary Search
- Priority Queue Overview
- Python Programming and Tools
- Top 30 Python Pattern Programs
- List vs Tuple
- Python Free Online Course
- Method Overriding in Python
- Top 21 Python Developer Skills
- Reverse a Number in Python
- Switch Case Functions in Python
- Info Retrieval System Overview
- Reverse a Number in Python
- Real-World Python Applications
- Data Science Careers and Comparisons
- Data Analyst Salary in India
- Data Scientist Salary in India
- Free Excel Certification Course
- Actuary Salary in India
- Data Analyst Interview Guide
- Pandas Interview Guide
- Tableau Filters Explained
- Data Mining Techniques Overview
- Data Analytics Lifecycle Phases
- Data Science Vs Analytics Comparison
- Artificial Intelligence and Machine Learning Projects
- Exciting IoT Project Ideas
- 16 Exciting AI Project Ideas
- 45+ Interesting ML Project Ideas
- Exciting Deep Learning Projects
- 12 Intriguing Linear Regression Projects
- 13 Neural Network Projects
- 5 Exciting Image Processing Projects
- Top 8 Thrilling AWS Projects
- 12 Engaging AI Projects in Python
- NLP Projects for Beginners
- Concepts and Algorithms in AIML
- Basic CNN Architecture Explained
- 6 Types of Regression Models
- Data Preprocessing Steps
- Bagging vs Boosting in ML
- Multinomial Naive Bayes Overview
- Gini Index for Decision Trees
- Bayesian Network Example
- Bayes Theorem Guide
- Top 10 Dimensionality Reduction Techniques
- Neural Network Step-by-Step Guide
- Technical Guides and Comparisons
- Make a Chatbot in Python
- Compute Square Roots in Python
- Permutation vs Combination
- Image Segmentation Techniques
- Generative AI vs Traditional AI
- AI vs Human Intelligence
- Random Forest vs Decision Tree
- Neural Network Overview
- Perceptron Learning Algorithm
- Selection Sort Algorithm
- Career and Practical Applications in AIML
- AI Salary in India Overview
- Biological Neural Network Basics
- Top 10 AI Challenges
- Production System in AI
- Top 8 Raspberry Pi Alternatives
- Top 8 Open Source Projects
- 14 Raspberry Pi Project Ideas
- 15 MATLAB Project Ideas
- Top 10 Python NLP Libraries
- Naive Bayes Explained
- Digital Marketing Projects and Strategies
- 10 Best Digital Marketing Projects
- 17 Fun Social Media Projects
- Top 6 SEO Project Ideas
- Digital Marketing Case Studies
- Coca-Cola Marketing Strategy
- Nestle Marketing Strategy Analysis
- Zomato Marketing Strategy
- Monetize Instagram Guide
- Become a Successful Instagram Influencer
- 8 Best Lead Generation Techniques
- Digital Marketing Careers and Salaries
- Digital Marketing Salary in India
- Top 10 Highest Paying Marketing Jobs
- Highest Paying Digital Marketing Jobs
- SEO Salary in India
- Brand Manager Salary in India
- Content Writer Salary Guide
- Digital Marketing Executive Roles
- Career in Digital Marketing Guide
- Future of Digital Marketing
- MBA in Digital Marketing Overview
- Digital Marketing Techniques and Channels
- 9 Types of Digital Marketing Channels
- Top 10 Benefits of Marketing Branding
- 100 Best YouTube Channel Ideas
- YouTube Earnings in India
- 7 Reasons to Study Digital Marketing
- Top 10 Digital Marketing Objectives
- 10 Best Digital Marketing Blogs
- Top 5 Industries Using Digital Marketing
- Growth of Digital Marketing in India
- Top Career Options in Marketing
- Interview Preparation and Skills
- 73 Google Analytics Interview Q&A
- 56 Social Media Marketing Q&A
- 78 Google AdWords Interview Q&A
- Top 133 SEO Interview Q&A
- 27+ Digital Marketing Q&A
- Digital Marketing Free Course
- Top 9 Skills for PPC Analysts
- Movies with Successful Social Media Campaigns
- Marketing Communication Steps
- Top 10 Reasons to Be an Affiliate Marketer
- Career Options and Paths
- Top 25 Highest Paying Jobs India
- Top 25 Highest Paying Jobs World
- Top 10 Highest Paid Commerce Job
- Career Options After 12th Arts
- Top 7 Commerce Courses Without Maths
- Top 7 Career Options After PCB
- Best Career Options for Commerce
- Career Options After 12th CS
- Top 10 Career Options After 10th
- 8 Best Career Options After BA
- Projects and Academic Pursuits
- 17 Exciting Final Year Projects
- Top 12 Commerce Project Topics
- Top 13 BCA Project Ideas
- Career Options After 12th Science
- Top 15 CS Jobs in India
- 12 Best Career Options After M.Com
- 9 Best Career Options After B.Sc
- 7 Best Career Options After BCA
- 22 Best Career Options After MCA
- 16 Top Career Options After CE
- Courses and Certifications
- 10 Best Job-Oriented Courses
- Best Online Computer Courses
- Top 15 Trending Online Courses
- Top 19 High Salary Certificate Courses
- 21 Best Programming Courses for Jobs
- What is SGPA? Convert to CGPA
- GPA to Percentage Calculator
- Highest Salary Engineering Stream
- 15 Top Career Options After Engineering
- 6 Top Career Options After BBA
- Job Market and Interview Preparation
- Why Should You Be Hired: 5 Answers
- Top 10 Future Career Options
- Top 15 Highest Paid IT Jobs India
- 5 Common Guesstimate Interview Q&A
- Average CEO Salary: Top Paid CEOs
- Career Options in Political Science
- Top 15 Highest Paying Non-IT Jobs
- Cover Letter Examples for Jobs
- Top 5 Highest Paying Freelance Jobs
- Top 10 Highest Paying Companies India
- Career Options and Paths After MBA
- 20 Best Careers After B.Com
- Career Options After MBA Marketing
- Top 14 Careers After MBA In HR
- Top 10 Highest Paying HR Jobs India
- How to Become an Investment Banker
- Career Options After MBA - High Paying
- Scope of MBA in Operations Management
- Best MBA for Working Professionals India
- MBA After BA - Is It Right For You?
- Best Online MBA Courses India
- MBA Project Ideas and Topics
- 11 Exciting MBA HR Project Ideas
- Top 15 MBA Project Ideas
- 18 Exciting MBA Marketing Projects
- MBA Project Ideas: Consumer Behavior
- What is Brand Management?
- What is Holistic Marketing?
- What is Green Marketing?
- Intro to Organizational Behavior Model
- Tech Skills Every MBA Should Learn
- Most Demanding Short Term Courses MBA
- MBA Salary, Resume, and Skills
- MBA Salary in India
- HR Salary in India
- Investment Banker Salary India
- MBA Resume Samples
- Sample SOP for MBA
- Sample SOP for Internship
- 7 Ways MBA Helps Your Career
- Must-have Skills in Sales Career
- 8 Skills MBA Helps You Improve
- Top 20+ SAP FICO Interview Q&A
- MBA Specializations and Comparative Guides
- Why MBA After B.Tech? 5 Reasons
- How to Answer 'Why MBA After Engineering?'
- Why MBA in Finance
- MBA After BSc: 10 Reasons
- Which MBA Specialization to choose?
- Top 10 MBA Specializations
- MBA vs Masters: Which to Choose?
- Benefits of MBA After CA
- 5 Steps to Management Consultant
- 37 Must-Read HR Interview Q&A
- Fundamentals and Theories of Management
- What is Management? Objectives & Functions
- Nature and Scope of Management
- Decision Making in Management
- Management Process: Definition & Functions
- Importance of Management
- What are Motivation Theories?
- Tools of Financial Statement Analysis
- Negotiation Skills: Definition & Benefits
- Career Development in HRM
- Top 20 Must-Have HRM Policies
- Project and Supply Chain Management
- Top 20 Project Management Case Studies
- 10 Innovative Supply Chain Projects
- Latest Management Project Topics
- 10 Project Management Project Ideas
- 6 Types of Supply Chain Models
- Top 10 Advantages of SCM
- Top 10 Supply Chain Books
- What is Project Description?
- Top 10 Project Management Companies
- Best Project Management Courses Online
- Salaries and Career Paths in Management
- Project Manager Salary in India
- Average Product Manager Salary India
- Supply Chain Management Salary India
- Salary After BBA in India
- PGDM Salary in India
- Top 7 Career Options in Management
- CSPO Certification Cost
- Why Choose Product Management?
- Product Management in Pharma
- Product Design in Operations Management
- Industry-Specific Management and Case Studies
- Amazon Business Case Study
- Service Delivery Manager Job
- Product Management Examples
- Product Management in Automobiles
- Product Management in Banking
- Sample SOP for Business Management
- Video Game Design Components
- Top 5 Business Courses India
- Free Management Online Course
- SCM Interview Q&A
- Fundamentals and Types of Law
- Acceptance in Contract Law
- Offer in Contract Law
- 9 Types of Evidence
- Types of Law in India
- Introduction to Contract Law
- Negotiable Instrument Act
- Corporate Tax Basics
- Intellectual Property Law
- Workmen Compensation Explained
- Lawyer vs Advocate Difference
- Law Education and Courses
- LLM Subjects & Syllabus
- Corporate Law Subjects
- LLM Course Duration
- Top 10 Online LLM Courses
- Online LLM Degree
- Step-by-Step Guide to Studying Law
- Top 5 Law Books to Read
- Why Legal Studies?
- Pursuing a Career in Law
- How to Become Lawyer in India
- Career Options and Salaries in Law
- Career Options in Law India
- Corporate Lawyer Salary India
- How To Become a Corporate Lawyer
- Career in Law: Starting, Salary
- Career Opportunities: Corporate Law
- Business Lawyer: Role & Salary Info
- Average Lawyer Salary India
- Top Career Options for Lawyers
- Types of Lawyers in India
- Steps to Become SC Lawyer in India
- Tutorials
- C Tutorials
- Recursion in C: Fibonacci Series
- Checking String Palindromes in C
- Prime Number Program in C
- Implementing Square Root in C
- Matrix Multiplication in C
- Understanding Double Data Type
- Factorial of a Number in C
- Structure of a C Program
- Building a Calculator Program in C
- Compiling C Programs on Linux
- Java Tutorials
- Handling String Input in Java
- Determining Even and Odd Numbers
- Prime Number Checker
- Sorting a String
- User-Defined Exceptions
- Understanding the Thread Life Cycle
- Swapping Two Numbers
- Using Final Classes
- Area of a Triangle
- Skills
- Software Engineering
- JavaScript
- Data Structure
- React.js
- Core Java
- Node.js
- Blockchain
- SQL
- Full stack development
- Devops
- NFT
- BigData
- Cyber Security
- Cloud Computing
- Database Design with MySQL
- Cryptocurrency
- Python
- Digital Marketings
- Advertising
- Influencer Marketing
- Search Engine Optimization
- Performance Marketing
- Search Engine Marketing
- Email Marketing
- Content Marketing
- Social Media Marketing
- Display Advertising
- Marketing Analytics
- Web Analytics
- Affiliate Marketing
- MBA
- MBA in Finance
- MBA in HR
- MBA in Marketing
- MBA in Business Analytics
- MBA in Operations Management
- MBA in International Business
- MBA in Information Technology
- MBA in Healthcare Management
- MBA In General Management
- MBA in Agriculture
- MBA in Supply Chain Management
- MBA in Entrepreneurship
- MBA in Project Management
- Management Program
- Consumer Behaviour
- Supply Chain Management
- Financial Analytics
- Introduction to Fintech
- Introduction to HR Analytics
- Fundamentals of Communication
- Art of Effective Communication
- Introduction to Research Methodology
- Mastering Sales Technique
- Business Communication
- Fundamentals of Journalism
- Economics Masterclass
- Free Courses
Top 10 Deep Learning Techniques You Should Know About
Updated on 01 March, 2024
30.48K+ views
• 13 min read
Table of Contents
Machine Learning and AI have changed the world around us for the last few years with its breakthrough innovation. Furthermore, it is the various deep learning techniques that take Machine Learning to a whole new level where machines can learn to discern tasks, inspired by the human brain’s neural network. It is the reason why we have voice control on our smartphones and TV remotes.
The following article will answer all your queries regarding deep learning technology, which is by far one of the most common machine learning techniques used in today’s world. This also includes the various real-time applications of this technology and the top ten algorithms of these popular machine learning methods.
What is Deep Learning?
Deep learning is currently one of the most popular machine learning techniques wherein computers are taught to perform specific tasks that come naturally to human beings. One basic example to help you get a better understanding of deep learning technology may include the use of voice control in devices such as hands-free speakers, phones, tablets, and TVs. In deep learning technology, computer models are trained to perform classification tasks from texts, images, and sound. It is the driving force behind various Artificial Intelligence applications and services that help improve the automation and performance of several physical and analytical tasks without human intervention.
Deep Learning Technology Applications
Deep learning is one of those machine learning methods that is constantly being used in our daily lives. However, more often than not, we are not aware of this complex data processing because it has been so well integrated into the products and services. On that note, here are some of the well-known applications of deep learning technology.
Customer Service
Various organizations have started adapting to these very popular machine learning methods, in their business operations, especially for improving all their customer service tasks. For example, chatbots, a straightforward form of Artificial Intelligence, can now be found across various customer service websites, applications, and services. With the advent of new innovative changes, there has also been an increase in sophisticated chatbot solutions that are able to provide multiple answers, even to ambiguous questions, through learning. Furthermore, the advent of various virtual assistants such as Siri, Alexa, and Google Assistant are some of the best examples of the application of deep learning technology.
Health Care Industry
Deep learning technology also has had a very important effect on the healthcare industry. Nowadays, various healthcare organizations have switched to the digitization of records and images to operate smoothly and eliminate any kind of manual error. Furthermore, the introduction of image recognition has also resulted in the analysis and assessment of a huge number of images in a much lesser amount of time.
Finance Industry
Last but not least, the use of predictive analytics in financial institutions has led to a series of benefits that might not have been possible otherwise. The said benefits include fraud detection, assessment of business risks for loan approval, and algorithmic trading of stocks.
Must Read: Free NLP course
There are different types of deep learning models that are both accurate and effectively tackle problems that are too complex for the human brain. Here’s how:
Top 10 Deep Learning Techniques
1. Classic Neural Networks
Also known as Fully Connected Neural Networks, it is often identified by its multilayer perceptrons, where the neurons are connected to the continuous layer. It was designed by Fran Rosenblatt, an American psychologist, in 1958. It involves the adaptation of the model into fundamental binary data inputs. There are three functions included in this model: they are:
- Linear function: Rightly termed, it represents a single line which multiplies its inputs with a constant multiplier.
- Non-Linear function: It is further divided into three subsets:
- Sigmoid Curve: It is a function interpreted as an S-shaped curve with its range from 0 to 1.
- Hyperbolic tangent (tanh) refers to the S-shaped curve having a range of -1 to 1.
- Rectified Linear Unit (ReLU): It is a single-point function that yields 0 when the input value is lesser than the set value and yields the linear multiple if the input is given is higher than the set value.
Works Best in:
- Any table dataset which has rows and columns formatted in CSV
- Classification and Regression issues with the input of real values
- Any model with the highest flexibility, like that of ANNS
2. Convolutional Neural Networks
CNN is an advanced and high-potential type of the classic artificial neural network model. It is built for tackling higher complexity, preprocessing, and data compilation. It takes reference from the order of arrangement of neurons present in the visual cortex of an animal brain.
The CNNs can be considered as one of the most efficiently flexible models for specializing in image as well as non-image data. These have four different organizations:
- It is made up of a single input layer, which generally is a two-dimensional arrangement of neurons for analyzing primary image data, which is similar to that of photo pixels.
- Some CNNs also consist of a single-dimensional output layer of neurons that processes images on their inputs, via the scattered connected convolutional layers.
- The CNNs also have the presence of a third layer known as the sampling layer to limit the number of neurons involved in the corresponding network layers.
- Overall, CNNs have single or multiple connected layers that connect the sampling to output layers.
This network model can help derive relevant image data in the form of smaller units or chunks. The neurons present in the convolution layers are accountable for the cluster of neurons in the previous layer.
Once the input data is imported into the convolutional model, there are four stages involved in building the CNN:
- Convolution: The process derives feature maps from input data, followed by a function applied to these maps.
- Max-Pooling: It helps CNN to detect an image based on given modifications.
- Flattening: In this stage, the data generated is then flattened for a CNN to analyze.
- Full Connection: It is often described as a hidden layer that compiles the loss function for a model.
The CNNs are adequate for tasks, including image recognition, image analyzing, image segmentation, video analysis, and natural language processing. However, there can be other scenarios where CNN networks can prove to be useful like:
- Image datasets containing OCR document analysis
- Any two-dimensional input data which can be further transformed to one-dimensional for quicker analysis
- The model needs to be involved in its architecture to yield output.
Read more: Convulational neural network
3. Recurrent Neural Networks (RNNs)
The RNNs were first designed to help predict sequences, for example, the Long Short-Term Memory (LSTM) algorithm is known for its multiple functionalities. Such networks work entirely on data sequences of the variable input length.
The RNN puts the knowledge gained from its previous state as an input value for the current prediction. Therefore, it can help in achieving short-term memory in a network, leading to the effective management of stock price changes, or other time-based data systems.
As mentioned earlier, there are two overall types of RNN designs that help in analyzing problems. They are:
- LSTMs: Useful in the prediction of data in time sequences, using memory. It has three gates: Input, Output, and Forget.
- Gated RNNs: Also useful in data prediction of time sequences via memory. It has two gates— Update and Reset.
Works Best in:
- One to One: A single input connected to a single output, like Image classification.
- One to many: A single input linked to output sequences, like Image captioning that includes several words from a single image.
- Many to One: Series of inputs generating single output, like Sentiment Analysis.
- Many to many: Series of inputs yielding series of outputs, like video classification.
It is also widely used in language translation, conversation modeling, and more.
Get best machine learning course online from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career.
Best Machine Learning and AI Courses Online
4. Generative Adversarial Networks
It is a combination of two deep learning techniques of neural networks – a Generator and a Discriminator. While the Generator Network yields artificial data, the Discriminator helps in discerning between a real and a false data.
Both of the networks are competitive, as the Generator keeps producing artificial data identical to real data – and the Discriminator continuously detecting real and unreal data. In a scenario where there’s a requirement to create an image library, the Generator network would produce simulated data to the authentic images. It would then generate a deconvolution neural network.
It would then be followed by an Image Detector network to differentiate between the real and fake images. Starting with a 50% accuracy chance, the detector needs to develop its quality of classification since the generator would grow better in its artificial image generation. Such competition would overall contribute to the network in its effectiveness and speed.
Works Best in:
- Image and Text Generation
- Image Enhancement
- New Drug Discovery processes
5. Self-Organizing Maps
The SOMs or Self-Organizing Maps operate with the help of unsupervised data that reduces the number of random variables in a model. In this type of deep learning technique, the output dimension is fixed as a two-dimensional model, as each synapse connects to its input and output nodes.
As each data point competes for its model representation, the SOM updates the weight of the closest nodes or Best Matching Units (BMUs). Based on the proximity of a BMU, the value of the weights changes. As weights are considered as a node characteristic in itself, the value represents the location of the node in the network.
Works best in:
- When the datasets don’t come with a Y-axis values
- Project explorations for analyzing the dataset framework
- Creative projects in Music, Videos, and Text with the help of AI
6. Boltzmann Machines
This network model doesn’t come with any predefined direction and therefore has its nodes connected in a circular arrangement. Because of such uniqueness, this deep learning technique is used to produce model parameters.
Different from all previous deterministic network models, the Boltzmann Machines model is referred to as stochastic.
Works Best in:
- System monitoring
- Setting up of a binary recommendation platform
- Analyzing specific datasets
Read: Step-by-Step Methods To Build Your Own AI System Today
7. Deep Reinforcement Learning
Before understanding the Deep Reinforcement Learning technique, reinforcement learning refers to the process where an agent interacts with an environment to modify its state. The agent can observe and take actions accordingly, the agent helps a network to reach its objective by interacting with the situation.
Here, in this network model, there is an input layer, output layer, and several hidden multiple layers – where the state of the environment is the input layer itself. The model works on the continuous attempts to predict the future reward of each action taken in the given state of the situation.
Works Best in:
- Board Games like Chess, Poker
- Self-Drive Cars
- Robotics
- Inventory Management
- Financial tasks like asset pricing
8. Autoencoders
One of the most commonly used types of deep learning techniques, this model operates automatically based on its inputs, before taking an activation function and final output decoding. Such a bottleneck formation leads to yielding lesser categories of data and leveraging most of the inherent data structures.
The Types of Autoencoders are:
- Sparse – Where hidden layers outnumber the input layer for the generalization approach to take place to reduce overfitting. It limits the loss function and prevents the autoencoder from overusing all its nodes.
- Denoising – Here, a modified version of inputs gets transformed into 0 at random.
- Contractive – Addition of a penalty factor to the loss function to limit overfitting and data copying, incase of hidden layer outnumbering input layer.
- Stacked – To an autoencoder, once another hidden layer gets added, it leads to two stages of encoding to that of one phase of decoding.
Works Best in:
- Feature detection
- Setting up a compelling recommendation model
- Add features to large datasets
9. Backpropagation
In deep learning, the backpropagation or back-prop technique is referred to as the central mechanism for neural networks to learn about any errors in data prediction. Propagation, on the other hand, refers to the transmission of data in a given direction via a dedicated channel. The entire system can work according to the signal propagation in the forward direction in the moment of decision, and sends back any data regarding shortcomings in the network, in reverse.
- First, the network analyzes the parameters and decides on the data
- Second, it is weighted out with a loss function
- Third, the identified error gets back-propagated to self-adjust any incorrect parameters
Works Best in:
- Data Debugging
Also read: 15 Interesting Machine Learning Project Ideas For Beginners
10. Gradient Descent
In the mathematical context, gradient refers to a slop that has a measurable angle and can be represented into a relationship between variables. In this deep learning technique, the relationship between the error produced in the neural network to that of the data parameters can be represented as “x” and “y”. Since the variables are dynamic in a neural network, therefore the error can be increased or decreased with small changes.
Many professionals visualize the technique as that of a river path coming down the mountain slopes. The objective of such a method is — to find the optimum solution. Since there is the presence of several local minimum solutions in a neural network, in which the data can get trapped and lead to slower, incorrect compilations – there are ways to refrain from such events.
As the terrain of the mountain, there are particular functions in the neural network called Convex Functions, which keeps the data flowing into expected rates and reach its most-minimum. There can be differences in methods taken by the data entering the final destination due to variation in initial values of the function.
Works Best in:
- Updating parameters in a given model
In-demand Machine Learning Skills
11. Self-Organizing Maps
Self-Organizing Maps, commonly referred to as SOMS is mainly used for data visualization. It significantly reduces the dimensions of data with the help of self-organizing neural networks. This is extremely useful, especially in cases where humans cannot easily interpret high-dimensional information.
Wrapping up
There are multiple deep learning techniques that come with its functionalities and practical approach. Once these models are identified and put in the right scenarios, it can lead to achieving high-end solutions based on the framework used by developers. Good luck!
Check out Master of Science in Machine Learning & AI with IIIT Bangalore, the best engineering school in the country to create a program that teaches you not only machine learning but also the effective deployment of it using the cloud infrastructure. Our aim with this program is to open the doors of the most selective institute in the country and give learners access to amazing faculty & resources in order to master a skill that is in high & growing
Popular AI and ML Blogs & Free Courses
Frequently Asked Questions (FAQs)
1. What are general adversarial networks?
It's a hybrid of two deep learning neural network techniques: Generators and Discriminators. While the Generator Network generates fictitious data, the Discriminator aids in distinguishing between actual and fictitious data. Because the Generator continues to produce false data that is identical to genuine data – and the Discriminator continues to recognize real and unreal data – both networks are competitive. The Generator network will generate simulation results to the authentic photographs in a case where an image library is required. After that, it would create a deconvolution neural network.
2. What is the use of self-organizing maps?
SOMs, or Self-Organizing Maps, work by reducing the number of random variables in a model by using unsupervised data. As each neuron connects to its inlet and outlet nodes, the result dimensionality is set as a two-dimensional model in this kind of deep learning technique. The SOM adjusts the value of the nearest nodes or Best Matching Units because each data point bids for its model representation (BMUs). The weights' value varies depending on how close a BMU is. Because weights are considered node characteristics in and of itself, the value signifies the node's position in the network.
3. What is backpropagation?
The back propagation algorithm or back-prop approach is the important requirement for neural nets to learn about any failures in data prediction in deep learning. On the other hand, propagation refers to the transfer of data in a specific direction across a defined channel. At the moment of choice, the complete system can work according to signal propagation in the forward direction, and sends back any data regarding network flaws in the reverse direction.
RELATED PROGRAMS