What are neural networks, and why are they important to learn about?

Neural networks, also known as neural nets or artificial neural networks (ANN), are machine learning algorithms organized in networks that mimic the functioning of neurons in the human brain. Using this biological neuron model, these systems are capable of unsupervised learning from massive datasets. This is an important enabler for artificial intelligence (AI) applications, which are used across a growing range of tasks including image recognition, natural language processing (NLP), and medical diagnosis. The related field of deep learning also relies on neural networks, typically using a convolutional neural network (CNN) architecture that connects multiple layers of neural networks in order to enable more sophisticated applications. For example, using deep learning, a facial recognition system can be created without specifying features such as eye and hair color; instead, the program can simply be fed thousands of images of faces and it will learn what to look for to identify different individuals over time, in much the same way that humans learn. Regardless of the end-use application, neural networks are typically created in TensorFlow and/or with Python programming skills.

What kinds of careers can I have with a background in neural networks?

Neural networks are a fundamental concept to understand for jobs in artificial intelligence (AI) and deep learning. And, as the number of industries seeking to leverage these approaches continues to grow, so do career opportunities for professionals with expertise in neural networks. For instance, these skills could lead to jobs in healthcare creating tools to automate X-ray scans or assist in drug discovery, or a job in the automotive industry developing autonomous vehicles. Professionals dedicating their careers to cutting-edge work in neural networks typically pursue a master’s degree or even a doctorate in computer science. This high-level expertise in neural networks and artificial intelligence are in high demand; according to the Bureau of Labor Statistics , computer research scientists earn a median annual salary of $122,840 per year, and these jobs are projected to grow much faster than average over the next decade.

Can I learn about neural networks by taking online courses in Coursera?

Absolutely - in fact, Coursera is one of the best places to learn about neural networks, online or otherwise. You can take courses and Specializations spanning multiple courses in topics like neural networks, artificial intelligence, and deep learning from pioneers in the field - including deeplearning.ai and Stanford University. Coursera has also partnered with industry leaders such as IBM, Google Cloud, and Amazon Web Services to offer courses that can lead to professional certificates in applied AI and other areas. You can even learn about neural networks with hands-on Guided Projects, a way to learn on Coursera by completing step-by-step tutorials led by experienced instructors.

What skills or experience do I need to already have, before starting to learn neural networks?

Before starting to learn neural networks, it's important to have experience creating and using algorithms since neural networks run on complicated algorithms. You should also have fundamental math skills at least, but you'll be at a better advantage if you have knowledge of linear algebra, calculus, statistics, and probability. Being proficient at problem-solving is also important before starting to learn neural networks. An understanding of how the human brain processes information is helpful since artificial neural networks are patterned after how the brain works. You'll also benefit from having experience using any programming language, in particular Java, R, Python, or C++. This includes experience using these languages' libraries, which you'll access to apply the algorithms used in neural networks.

What kinds of people are best suited for roles in neural networks?

People who are best suited for roles in neural networks are innovative, interested in technology, and have the ability to identify patterns in large amounts of data and draw conclusions from them. People who have a desire to make life and work easier for human beings through artificial technology are well suited for roles in neural networks too. Also, people who have good programming skills and data engineering skills like SQL, data analysis, ETL, and data visualization are likely well suited for roles in neural networks.

How do I know if learning neural networks is right for me?

If you are interested in the field of artificial intelligence, learning about neural networks is right for you. If your current or future position involves data analysis, pattern recognition, optimization, forecasting, or decision-making, you might also benefit from learning neural networks. Neural networks are also used in image recognition software, speech synthesis, self-driving vehicles, navigation systems, industrial robots, and algorithms for protecting information systems, so if you're interested in these technologies, learning neural networks may be helpful to you.

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Arizona State University
Master of Computer Science
Skills you'll gain: Machine Learning, Theoretical Computer Science, BlockChain, Finance, Software Engineering, Algorithms, Security Engineering, Software Testing, Databases, Data Visualization, Data Management, Computer Architecture, Computer Networking, Computer Programming, Network Security, Software Architecture, Computational Logic, Computational Thinking, Cryptography, Deep Learning, Mathematics, NoSQL, SQL, Statistical Programming, Mobile Development, Data Analysis, Data Mining, Data Structures, Distributed Computing Architecture, General Statistics, Mathematical Theory & Analysis, Probability & Statistics, Operating Systems, Security Strategy, System Security, Amazon Web Services, Calculus, Cloud Computing, Communication, Data Model, Database Administration, Database Application, Database Design, Dimensionality Reduction, Feature Engineering, Geovisualization, Hardware Design, Journalism, Microarchitecture, Programming Principles, Statistical Machine Learning, Advertising, Algebra, Application Development, Artificial Neural Networks, Bayesian Network, Computer Graphics, Computer Vision, Critical Thinking, Design and Product, Docker (Software), Econometrics, Entrepreneurship, Human Computer Interaction, Leadership and Management, Marketing, Matlab, Operations Research, Other Programming Languages, Planning, Probability Distribution, Product Design, Python Programming, Research and Design, Scala Programming, Software Framework, Spreadsheet Software, Statistical Tests, Strategy and Operations, Supply Chain Systems, Supply Chain and Logistics, System Programming, Tableau Software, User Experience, Web Development, iOS Development
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Degree · 1-4 Years
University of Colorado Boulder
Master of Science in Data Science
Skills you'll gain: Theoretical Computer Science, Probability & Statistics, General Statistics, Algorithms, Data Management, Computer Architecture, Mathematics, Strategy and Operations, Databases, Hardware Design, Statistical Programming, Communication, Leadership and Management, Machine Learning, Research and Design, Operating Systems, SQL, Writing, Data Analysis, Data Structures, Business Communication, Probability Distribution, Computer Programming, Project Management, Regression, Database Design, Entrepreneurship, Software Engineering, Business Analysis, Computer Graphics, Computer Networking, Data Visualization, Design and Product, Data Model, Database Application, Database Theory, Machine Learning Algorithms, Statistical Analysis, Statistical Machine Learning, Systems Design, Human Computer Interaction, Database Administration, Estimation, Problem Solving, Operations Research, Statistical Tests, Internet Of Things, Network Architecture, Computer Vision, PostgreSQL, Deep Learning, Geometry, Security Engineering, Applied Mathematics, Marketing, Computer Graphic Techniques, Cryptography, Accounting, Finance, Graph Theory, Mathematical Theory & Analysis, Programming Principles, Python Programming, Interactive Design, User Experience, Business Psychology, Critical Thinking, Data Mining, Correlation And Dependence, Distributed Computing Architecture, Linear Algebra, Supply Chain and Logistics, Algebra, User Experience Design, Differential Equations, Cost Accounting, Cloud Computing, Security Strategy, Computational Logic, Scrum (Software Development), Applied Machine Learning, Calculus, Econometrics, Feature Engineering, Graphic Design, Other Programming Languages, Sales, Software Architecture, Software Testing, System Programming, Visual Design, Artificial Neural Networks, Market Analysis, NoSQL, Statistical Visualization, Data Warehousing, Financial Analysis, Strategy, Basic Descriptive Statistics, Computational Thinking, Data Analysis Software, Exploratory Data Analysis, Material Handling, Product Lifecycle, Risk Management, Amazon Web Services, Big Data, Cloud Platforms, Culture, Cyberattacks, Decision Making, Graphics Software, Human Resources, Microarchitecture, Computer Security Models, Network Model, Operational Analysis, Reinforcement Learning, Software Security, System Security, User Research, Plot (Graphics), R Programming, Account Management, Banking, BlockChain, Budget Management, Business Process Management, C Programming Language Family, Computer Programming Tools, Data Architecture, Experiment, FinTech, Financial Accounting, Financial Management, Geovisualization, Markov Model, Matlab, Natural Language Processing, Operations Management, Organizational Development, Planning, Product Management, Spreadsheet Software, Storytelling, Computer Science, Computer Security Incident Management, Data Science, Dimensionality Reduction, Forecasting, Leadership Development, Linux, Network Analysis, Network Security, System Software
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University of Colorado Boulder
Master of Engineering in Engineering Management
Skills you'll gain: Theoretical Computer Science, Probability & Statistics, General Statistics, Algorithms, Data Management, Computer Architecture, Mathematics, Strategy and Operations, Databases, Hardware Design, Statistical Programming, Communication, Leadership and Management, Machine Learning, Research and Design, Operating Systems, SQL, Writing, Data Analysis, Data Structures, Business Communication, Probability Distribution, Computer Programming, Project Management, Regression, Database Design, Entrepreneurship, Software Engineering, Business Analysis, Computer Graphics, Computer Networking, Data Visualization, Design and Product, Data Model, Database Application, Database Theory, Machine Learning Algorithms, Statistical Analysis, Statistical Machine Learning, Systems Design, Human Computer Interaction, Database Administration, Estimation, Problem Solving, Operations Research, Statistical Tests, Internet Of Things, Network Architecture, Computer Vision, PostgreSQL, Deep Learning, Geometry, Security Engineering, Applied Mathematics, Marketing, Computer Graphic Techniques, Cryptography, Accounting, Finance, Graph Theory, Mathematical Theory & Analysis, Programming Principles, Python Programming, Interactive Design, User Experience, Business Psychology, Critical Thinking, Data Mining, Correlation And Dependence, Distributed Computing Architecture, Linear Algebra, Supply Chain and Logistics, Algebra, User Experience Design, Differential Equations, Cost Accounting, Cloud Computing, Security Strategy, Computational Logic, Scrum (Software Development), Applied Machine Learning, Calculus, Econometrics, Feature Engineering, Graphic Design, Other Programming Languages, Sales, Software Architecture, Software Testing, System Programming, Visual Design, Artificial Neural Networks, Market Analysis, NoSQL, Statistical Visualization, Data Warehousing, Financial Analysis, Strategy, Basic Descriptive Statistics, Computational Thinking, Data Analysis Software, Exploratory Data Analysis, Material Handling, Product Lifecycle, Risk Management, Amazon Web Services, Big Data, Cloud Platforms, Culture, Cyberattacks, Decision Making, Graphics Software, Human Resources, Microarchitecture, Computer Security Models, Network Model, Operational Analysis, Reinforcement Learning, Software Security, System Security, User Research, Plot (Graphics), R Programming, Account Management, Banking, BlockChain, Budget Management, Business Process Management, C Programming Language Family, Computer Programming Tools, Data Architecture, Experiment, FinTech, Financial Accounting, Financial Management, Geovisualization, Markov Model, Matlab, Natural Language Processing, Operations Management, Organizational Development, Planning, Product Management, Spreadsheet Software, Storytelling, Computer Science, Computer Security Incident Management, Data Science, Dimensionality Reduction, Forecasting, Leadership Development, Linux, Network Analysis, Network Security, System Software
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Degree · 1-4 Years
University of Colorado Boulder
Master of Science in Electrical Engineering
Skills you'll gain: Theoretical Computer Science, Probability & Statistics, General Statistics, Algorithms, Data Management, Computer Architecture, Mathematics, Strategy and Operations, Databases, Hardware Design, Statistical Programming, Communication, Leadership and Management, Machine Learning, Research and Design, Operating Systems, SQL, Writing, Data Analysis, Data Structures, Business Communication, Probability Distribution, Computer Programming, Project Management, Regression, Database Design, Entrepreneurship, Software Engineering, Business Analysis, Computer Graphics, Computer Networking, Data Visualization, Design and Product, Data Model, Database Application, Database Theory, Machine Learning Algorithms, Statistical Analysis, Statistical Machine Learning, Systems Design, Human Computer Interaction, Database Administration, Estimation, Problem Solving, Operations Research, Statistical Tests, Internet Of Things, Network Architecture, Computer Vision, PostgreSQL, Deep Learning, Geometry, Security Engineering, Applied Mathematics, Marketing, Computer Graphic Techniques, Cryptography, Accounting, Finance, Graph Theory, Mathematical Theory & Analysis, Programming Principles, Python Programming, Interactive Design, User Experience, Business Psychology, Critical Thinking, Data Mining, Correlation And Dependence, Distributed Computing Architecture, Linear Algebra, Supply Chain and Logistics, Algebra, User Experience Design, Differential Equations, Cost Accounting, Cloud Computing, Security Strategy, Computational Logic, Scrum (Software Development), Applied Machine Learning, Calculus, Econometrics, Feature Engineering, Graphic Design, Other Programming Languages, Sales, Software Architecture, Software Testing, System Programming, Visual Design, Artificial Neural Networks, Market Analysis, NoSQL, Statistical Visualization, Data Warehousing, Financial Analysis, Strategy, Basic Descriptive Statistics, Computational Thinking, Data Analysis Software, Exploratory Data Analysis, Material Handling, Product Lifecycle, Risk Management, Amazon Web Services, Big Data, Cloud Platforms, Culture, Cyberattacks, Decision Making, Graphics Software, Human Resources, Microarchitecture, Computer Security Models, Network Model, Operational Analysis, Reinforcement Learning, Software Security, System Security, User Research, Plot (Graphics), R Programming, Account Management, Banking, BlockChain, Budget Management, Business Process Management, C Programming Language Family, Computer Programming Tools, Data Architecture, Experiment, FinTech, Financial Accounting, Financial Management, Geovisualization, Markov Model, Matlab, Natural Language Processing, Operations Management, Organizational Development, Planning, Product Management, Spreadsheet Software, Storytelling, Computer Science, Computer Security Incident Management, Data Science, Dimensionality Reduction, Forecasting, Leadership Development, Linux, Network Analysis, Network Security, System Software
Earn a degree
Degree · 1-4 Years
Johns Hopkins University
GPU Programming
Skills you'll gain: Computer Programming, C++ Programming, Computer Graphic Techniques, Computer Graphics, C Programming Language Family, Computer Architecture, Distributed Computing Architecture, Python Programming, Statistical Programming, Theoretical Computer Science, Computational Thinking, Other Programming Languages, Algorithms, Data Science, Linear Algebra, Machine Learning
3.2
(19 reviews)
Intermediate · Specialization · 3-6 Months
DeepLearning.AI
Machine Learning Data Lifecycle in Production
Skills you'll gain: Python Programming, Statistical Programming, Tensorflow, Exploratory Data Analysis, Feature Engineering, Machine Learning, Probability & Statistics, Applied Machine Learning, Data Management, Data Warehousing, Extract, Transform, Load, Business Analysis, Computer Networking, Data Analysis, Data Visualization, Network Security, Security Engineering, Security Strategy, Statistical Visualization
4.3
(568 reviews)
Advanced · Course · 1-4 Weeks

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Master of Computer Science: Arizona State University
Master of Science in Data Science: University of Colorado Boulder
Master of Engineering in Engineering Management: University of Colorado Boulder
Master of Science in Electrical Engineering: University of Colorado Boulder
Identify Horses or Humans with TensorFlow and Vertex AI: Google Cloud
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Skills you can learn in Machine Learning

Python Programming (33)

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Reinforcement Learning (13)

Algebra (10)

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Numpy (9)

Frequently Asked Questions about Neural Networks

Neural networks, also known as neural nets or artificial neural networks (ANN), are machine learning algorithms organized in networks that mimic the functioning of neurons in the human brain. Using this biological neuron model, these systems are capable of unsupervised learning from massive datasets.
This is an important enabler for artificial intelligence (AI) applications, which are used across a growing range of tasks including image recognition, natural language processing (NLP), and medical diagnosis. The related field of deep learning also relies on neural networks, typically using a convolutional neural network (CNN) architecture that connects multiple layers of neural networks in order to enable more sophisticated applications.
For example, using deep learning, a facial recognition system can be created without specifying features such as eye and hair color; instead, the program can simply be fed thousands of images of faces and it will learn what to look for to identify different individuals over time, in much the same way that humans learn. Regardless of the end-use application, neural networks are typically created in TensorFlow and/or with Python programming skills.‎
Neural networks are a fundamental concept to understand for jobs in artificial intelligence (AI) and deep learning. And, as the number of industries seeking to leverage these approaches continues to grow, so do career opportunities for professionals with expertise in neural networks. For instance, these skills could lead to jobs in healthcare creating tools to automate X-ray scans or assist in drug discovery, or a job in the automotive industry developing autonomous vehicles.
Professionals dedicating their careers to cutting-edge work in neural networks typically pursue a master’s degree or even a doctorate in computer science. This high-level expertise in neural networks and artificial intelligence are in high demand; according to the Bureau of Labor Statistics, computer research scientists earn a median annual salary of $122,840 per year, and these jobs are projected to grow much faster than average over the next decade.‎
Absolutely - in fact, Coursera is one of the best places to learn about neural networks, online or otherwise. You can take courses and Specializations spanning multiple courses in topics like neural networks, artificial intelligence, and deep learning from pioneers in the field - including deeplearning.ai and Stanford University. Coursera has also partnered with industry leaders such as IBM, Google Cloud, and Amazon Web Services to offer courses that can lead to professional certificates in applied AI and other areas. You can even learn about neural networks with hands-on Guided Projects, a way to learn on Coursera by completing step-by-step tutorials led by experienced instructors.‎
Before starting to learn neural networks, it's important to have experience creating and using algorithms since neural networks run on complicated algorithms. You should also have fundamental math skills at least, but you'll be at a better advantage if you have knowledge of linear algebra, calculus, statistics, and probability. Being proficient at problem-solving is also important before starting to learn neural networks. An understanding of how the human brain processes information is helpful since artificial neural networks are patterned after how the brain works. You'll also benefit from having experience using any programming language, in particular Java, R, Python, or C++. This includes experience using these languages' libraries, which you'll access to apply the algorithms used in neural networks.‎
People who are best suited for roles in neural networks are innovative, interested in technology, and have the ability to identify patterns in large amounts of data and draw conclusions from them. People who have a desire to make life and work easier for human beings through artificial technology are well suited for roles in neural networks too. Also, people who have good programming skills and data engineering skills like SQL, data analysis, ETL, and data visualization are likely well suited for roles in neural networks.‎
If you are interested in the field of artificial intelligence, learning about neural networks is right for you. If your current or future position involves data analysis, pattern recognition, optimization, forecasting, or decision-making, you might also benefit from learning neural networks. Neural networks are also used in image recognition software, speech synthesis, self-driving vehicles, navigation systems, industrial robots, and algorithms for protecting information systems, so if you're interested in these technologies, learning neural networks may be helpful to you.‎

This FAQ content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

Filter by

482 results for "neural networks"

Master of Computer Science

Master of Science in Data Science

Master of Engineering in Engineering Management

Master of Science in Electrical Engineering

Identify Horses or Humans with TensorFlow and Vertex AI

MNIST Fashion Datensatz mit Tensorflow

Bildklassifizierung mit Tensorflow

GPU Programming

Machine Learning Data Lifecycle in Production

Machine Learning: Concepts and Applications

Ein Crashkurs in Datenwissenschaft

Medical Insurance Premium Prediction with Machine Learning

Searches related to neural networks

In summary, here are 10 of our most popular neural networks courses

Skills you can learn in Machine Learning

Frequently Asked Questions about Neural Networks

What are neural networks, and why are they important to learn about?‎

What kinds of careers can I have with a background in neural networks?‎

Can I learn about neural networks by taking online courses in Coursera?‎

What skills or experience do I need to already have, before starting to learn neural networks?‎

What kinds of people are best suited for roles in neural networks?‎

How do I know if learning neural networks is right for me?‎

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Filter by

482 results for "neural networks"

Searches related to neural networks

In summary, here are 10 of our most popular neural networks courses

Skills you can learn in Machine Learning

Frequently Asked Questions about Neural Networks

What are neural networks, and why are they important to learn about?‎

What kinds of careers can I have with a background in neural networks?‎

Can I learn about neural networks by taking online courses in Coursera?‎

What skills or experience do I need to already have, before starting to learn neural networks?‎

What kinds of people are best suited for roles in neural networks?‎

How do I know if learning neural networks is right for me?‎

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