- 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
Deep Learning vs Neural Networks: Difference Between Deep Learning and Neural Networks
Updated on 22 November, 2022
32.03K+ views
• 13 min read
Table of Contents
Artificial Intelligence and Machine Learning have come a long way since their conception in the late 1950s. Today, these technologies have become immensely sophisticated and advanced. However, while technological strides in the Data Science domain are more than welcome, it has brought forth a slew of terminologies that are beyond the understanding of common man.
Best Machine Learning and AI Courses Online
In fact, even many businesses leveraging disruptive technologies like AI and ML cannot tell apart many technological terminologies.
The core cause of confusion around the new terminologies brought about by Data Science is because Data Science concepts are deeply entwined with one another – they are inter-related in many aspects.
That’s why we often hear and see the people around us using the terms “Artificial Intelligence,” “Machine Learning” and “Deep Learning” interchangeably. However, despite the conceptual similarities, these technologies are unique in their own way.
Today, we will address one of the less highlighted matters in Data Science – the Deep Learning vs Neural Network debate.
In-demand Machine Learning Skills
Before we venture in deep into the Deep Learning vs Neural Network debate, we must understand what these concepts mean individually.
Check out our free deep learning courses
What is Deep Learning?
Deep Learning or Hierarchical Learning is a subset of Machine Learning in Artificial Intelligence that can imitate the data processing function of the human brain and create similar patterns the brain used for decision making. Contrary to task-based algorithms, Deep Learning systems learn from data representations – they can learn from unstructured or unlabeled data.
Deep Learning architectures like deep neural networks, belief networks, and recurrent neural networks, and convolutional neural networks have found applications in the field of computer vision, audio/speech recognition, machine translation, social network filtering, bioinformatics, drug design and so much more.
Examples of deep learning in practical scenarios
Plenty of industries are using deep learning to explore its benefits. The following section discusses some of the prominent examples:
1. Medical research: Cancer researchers use deep learning to automatically detect cancer cells.
2. Electronics: Deep learning is extensively used in automated speech translation. It is used in home assistance devices that respond to your voice and recognize your preferences.
3. Automated Driving: Automotive researchers can now automatically identify objects like stop signs, traffic lights, etc., using deep learning. They also use deep learning and artificial neural network to detect pedestrians, which help reduce accidents.
Key advantages of using deep learning
1. Independent of feature engineering:
Feature engineering is fundamental in machine learning. The reason is it enhances accuracy, and occasionally the process can need domain knowledge on a specific issue. One of the greatest benefits of using the deep learning concept is its potential to implement feature engineering on its own. It involves an algorithm that scans the data to recognize features that correlate and then merge them to facilitate faster learning without being explicitly instructed to do that. As a result, deep learning and artificial neural network reduce manual efforts for data scientists.
2. Maximum use of unstructured data:
A massive proportion of an organization’s data is unstructured since most of it exists in various formats like text, images, etc. Most machine learning algorithms find it challenging to analyze unstructured data. This implies that it stays unused and is where deep learning proves useful. You can use various data formats to train deep learning algorithms and gain valuable insights useful to the training’s purpose.
3. Can provide high-quality results:
Humans are bound to make mistakes. But once the neural networks are properly trained, a deep learning model can accomplish thousands of repetitive tasks in a comparatively shorter duration of time than what it takes for humans.
4. Removes unnecessary costs:
Recalls are quite costly. A recall can incur an organization millions of dollars in some industries. Deep learning helps organizations help to detect subjective defects which are difficult to train, for example, product labeling errors. Moreover, deep learning models can recognize defects that may be difficult to recognize otherwise.
Consistent images may become challenging due to various reasons. Deep learning can account for those variations in such cases and implement valuable features to make the assessments robust. This benefit of deep learning helps you to compare deep learning vs neural networks.
5. Removes the need for data labeling:
Data labeling can be a time-consuming and expensive job. The deep learning approach removes the need for well-labeled data. The reason is that the relevant algorithms can be learned without any instruction. Several other types of machine learning algorithms are not as successful as deep learning.
What is a Neural Network?
A Neural Networks is made of an assortment of algorithms that are modelled on the human brain. These algorithms can interpret sensory data via machine perception and label or cluster the raw data. They are designed to recognize numerical patterns that are contained in vectors within which all the real-world data (images, sound, text, time series, etc.) has to be translated.
Essentially, the primary task of a Neural Networks is to cluster and classify the raw data – they group the unlabeled data based on the similarities found in the input data and then classify the data based on the labelled training dataset. Neural Networks can automatically adapt to changing input. So, you need not redesign the output criteria each time the input changes to generate the best possible result.
Why should you use neural networks?
- They help to plot the complex and nonlinear relationships of real-world scenarios.
- They can generalize, and therefore, they are used in pattern recognition.
- They are used in various applications like signature identification, text summarization, handwriting recognition, and more.
- They can model data with superior volatility.
Benefits of neural networks:
- Neural Networks can learn by themselves and generate output that is not restricted to the provided input.
- The input is saved in their networks rather than a database. So, data loss doesn’t influence its working.
- They can learn from examples and implement them when similar events happen. So, they are useful in real-time events.
- The network can identify the fault and still generate the output, although if a neuron is not responding or information is missing.
- The neural network machine learning can carry out multiple tasks in parallel without impacting the overall system performance.
- It has a broad scope in the future. The researchers are continuously working on the latest technologies dependent on neural networks.
- Automation is gradually becoming more prevalent, so neural network machine learning is more efficient at handling changes and adapting accordingly.
- There are more job openings for neural network experts. So, it is expected that neural networks-related jobs would be ample in the future.
Deep Learning vs Neural Network
While Deep Learning incorporates Neural Networks within its architecture, there’s a stark difference between Deep Learning and Neural Networks. Here we’ll shed light on the three major points of difference between Deep Learning and Neural Networks.
1. Definition
Neural Networks – It is a structure consisting of ML algorithms wherein the artificial neurons make the core computational unit that focuses on uncovering the underlying patterns or connections within a dataset, just like the human brain does while decision making.
Deep Learning – It is a branch of Machine Learning that leverages a series of nonlinear processing units comprising multiple layers for feature transformation and extraction. It has several layers of artificial neural networks that carry out the ML process. The first layer of the neural network processes the raw data input and passes the information to the second layer. Deep Learning Career Path: Top Fascinating Job Roles
The second later then processes that information further by adding additional information (for example, user’s IP address) and passes it to the next layer. This process continues throughout all layers of the Deep Learning network until the desired result is achieved.
2. Structure
A Neural Network consists of the following components:
- Neurons – A neuron is a mathematical function designed to imitate the functioning of a biological neuron. It computes the weighted average of the data input and passes the information through a nonlinear function, a.k.a. The activation function (for examples, the sigmoid).
- Connection and weights – As the name suggests, connections connect a neuron in one layer to another neuron in the same layer or another layer. Each connection has a weight value linked to it. Here, a weight represents the strength of the connection between the units. The aim is to reduce the weight value to decrease the possibilities of loss (error).
- Propagation function – Two propagation functions work in a Neural Network: forward propagation that delivers the “predicted value” and backward propagation that delivers the “error value.”
- Learning rate – Neural Networks are trained using Gradient Descent to optimize the weights. Back-propagation is used at each iteration to calculate the derivative of the loss function in reference to each weight value and subtract it from that weight. Learning rate decides how quickly or slowly you want to update the weight (parameter) values of the model.
A Deep Learning model consists of the following components:
- Motherboard – The motherboard chipset of the model is usually based on PCI-e lanes.
- Processors – The GPU required for Deep Learning must be determined according to the number of cores and cost of the processor.
- RAM – This is the physical memory and storage. Since Deep Learning algorithms demand greater CPU usage and storage area, the RAM must be huge.
- PSU – As the memory demands increase, it becomes crucial to employ a large PSU that can handle massive and complex Deep Learning functions.
Get Machine Learning Training online from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career.
3. Architecture
The architecture of a Neural Network includes:
- Feed Forward Neural Networks – This is the most common kind of Neural Network architecture wherein the first layer is the input layer, and the final layer is the output layer. All intermediary layers are hidden layers.
- Recurrent Neural Networks – This network architecture is a series of artificial neural networks wherein the connections between nodes make a directed graph along a temporal sequence. Hence, this type of network depicts temporal dynamic behaviour.
- Symmetrically Connected Neural Networks – These are similar to recurrent neural networks with the only difference being that in Symmetrically Connected Neural Networks, the connections between units are symmetrical (they have the same weight values in both directions). They are constrained in nature compared to a recurrent neural network because they use energy functions. Symmetrically connected networks with hidden networks are called Boltzmann machines, whereas those without the hidden network are called Hopfield nets.
The architecture of a Deep Learning model includes:
- Unsupervised Pre-trained Networks – As the name suggests, this architecture need no formal training since it is pre-trained on past experiences. These include Autoencoders, Deep Belief Networks, and Generative Adversarial Networks.
- Convolutional Neural Networks – This is a Deep Learning algorithm that can take in an input image, assign importance (learnable weights and biases) to different objects in the image, and also differentiate between those objects. It targets learning higher-order features through convolutions which improve the identification user experience and image recognition. This architecture simplifies the identification of street signs, faces, platypuses, and other substances.
- Recurrent Neural Networks – Recurrent Neural Networks refer to a specific kind of artificial neural network that adds additional weights to the network to create cycles in the network graph so as to maintain an internal state. A recurrent neural network belongs to the family of feedforward that believes in sending their information over time steps.
- They present information about how that hierarchical structure is upheld in the dataset.Recursive Neural Networks – This is a type of Deep Neural Network that is created by applying the same set of weights recursively over a structured input, to produce a structured prediction over or a scalar prediction on variable-size input structures by passing a topological structure.
4. Time and Accuracy
Generally, it takes less time to train neural networks. They feature lower accuracy than deep learning approaches.
It takes more time to train deep learning models. They feature higher accuracy than neural networks. This is the prominent difference between deep learning vs neural networks.
5. Critique
Neural network criticism is dependent on theoretical problems, training problems, hardware problems, hybrid techniques, and real-world examples of criticisms. On the other hand, deep learning criticism is based on errors, theory, cyber threats, etc. This point of deep learning vs neural networks difference gives you a clear idea of which model to use based on the problem.
6. Task interpretation
Neural networks poorly interpret your tasks whereas the deep learning network more effectively interprets your task.
7. Application areas:
The greatest point of comparison between deep learning vs neural networks is the applications.
The neural network models are used for the following applications:
- System identification
- Natural resource management
- Process control
- Vehicle control
- Quantum chemistry
- Decision making
- Game playing
- Pattern recognition
- Face identification
- Signal classification
- Sequence recognition
- Data mining
- Machine translation
- Email spam filtering
- Social network filtering, etc.
The deep learning models are used for the following applications:
- Image recognition
- Automatic speech recognition
- Natural language processing
- Visual art processing
- Drug discovery and toxicology
- Customer relationship management
- Mobile advertising
- Recommendation engines
- Bioinformatics
- Image restoration etc.
Conclusion
Since Deep Learning and Neural Networks are so deeply intertwined, it is difficult to tell them apart from each other on the surface level. However, by now, you’ve understood that there’s a significant difference between Deep Learning and Neural Networks.
While Neural Networks use neurons to transmit data in the form of input values and output values through connections, Deep Learning is associated with the transformation and extraction of feature which attempts to establish a relationship between stimuli and associated neural responses present in the brain.
If you are interested to know more about deep learning and artificial intelligence, check out our Executive PG Programme in Machine Learning & AI program which is designed for working professionals and more than 450 hours of rigorositeus training.
Popular AI and ML Blogs & Free Courses
Frequently Asked Questions (FAQs)
1. What is the difference between Deep Learning and Machine Learning?
Both deep learning and machine learning are specialized areas in the vast field of artificial intelligence. Machine learning is essentially a subset of AI that deals with how computers or machines can be made to learn to perform specific tasks with minimal human intervention. Now, deep learning is a highly sophisticated subset of machine learning. Deep learning is based on artificial neural networks, which help computers understand and decide like the human brain. Machine learning generally requires structured data inputs whereas deep learning can process greater volumes of unstructured data inputs. Also, while machine learning still requires some human intervention, deep learning models require minimal to zero human interference.
2. What are some examples of deep learning in our daily lives?
It is interesting to note that deep learning is used in many applications that we come across in our day-to-day lives. Some of the most common deep learning applications are virtual assistants like Alexa, Siri, and Cortana. These virtual or digital assistants can understand our voice commands and translate them to process and perform specific actions using deep learning. Then, chatbots and service bots in customer service departments also employ deep learning. Facial recognition in social media platforms, navigation in driverless cars, shopping, entertainment, and even pharmaceutical apps use deep learning to provide greater convenience to customers.
3. Is machine learning a good career choice?
If you like to learn and work with data, algorithms, automation, and even programming languages to some extent, then a career in machine learning can be a good option for you. There is no doubt about the fact that today, there is a huge demand and low supply of properly trained and experienced machine learning experts in the market. So, machine learning is undoubtedly a good career choice in terms of general demand, career growth, salary, and job prospects.
4. What do you understand by the term deep learning?
Deep Learning, also known as Hierarchical Learning, is a subset of ML in AI that can mimic the human brain's data processing function and generate similar decision-making patterns. Deep Learning systems, unlike task-based algorithms, learn from data models — they can train from unorganized or unlabeled data. Deep Learning architectures such as deep learning models, belief networks, and RNNs, as well as convolutional neural networks, have found use in object recognition, sound recognition, translation software, social network filtering, bioinformatics, drug design, and many other fields.
5. What is a neural network?
A Neural Network is made up of a collection of algorithms modelled after the human brain. These algorithms can use machine perception to evaluate sensory data and classify or group the raw data. They're made to recognize numerical patterns in vectors, from which all real-world data (images, music, text, time series, and so on) must be translated. The fundamental objective of a Neural Network is to cluster and categorize unlabeled data based on similarities detected in the input data, and then to organize the data based on the tagged training dataset.
6. What are the core differences between deep learning and neural networks?
Neural Networks are a structure made up of machine learning algorithms in which artificial neurons serve as the central computing unit, focusing on identifying hidden patterns or connections in a dataset, much the same as the human brain does when making decisions. Deep Learning is a type of Machine Learning that involves a set of nonlinear processing units with numerous layers to transform and extract features. The ML process is carried out by numerous layers of artificial neural networks. The raw data input is processed by the first layer of the neural network, which then transfers the data to the second layer.
RELATED PROGRAMS