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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University of Colorado Boulder
Master of Science in Data Science
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University of Colorado Boulder
Master of Engineering in Engineering Management
Habilidades que obtendrás: Theoretical Computer Science, Probability & Statistics, General Statistics, Algorithms, Data Management, Strategy and Operations, Computer Architecture, Mathematics, Leadership and Management, Databases, Data Analysis, Communication, Hardware Design, Operating Systems, Statistical Programming, Computer Programming, Machine Learning, Research and Design, Business Analysis, Finance, Regression, SQL, Writing, Project Management, Data Structures, Statistical Analysis, Business Communication, Experiment, Entrepreneurship, Probability Distribution, Software Engineering, Database Design, Statistical Tests, Computer Networking, Accounting, Computer Graphics, Design and Product, Systems Design, Data Visualization, Data Model, Database Administration, Database Application, Database Theory, Machine Learning Algorithms, Operations Research, Statistical Machine Learning, Human Computer Interaction, Estimation, Internet Of Things, PostgreSQL, Problem Solving, Network Architecture, Graph Theory, Applied Mathematics, Computer Vision, Deep Learning, Programming Principles, Geometry, User Experience, Econometrics, Marketing, Mathematical Theory & Analysis, Python Programming, Supply Chain and Logistics, Computer Graphic Techniques, Algebra, Cryptography, Business Psychology, Interactive Design, Budget Management, Correlation And Dependence, Distributed Computing Architecture, Financial Analysis, Linear Algebra, Linux, Critical Thinking, Security Engineering, Data Mining, Cost Accounting, Differential Equations, User Experience Design, Data Warehousing, Network Model, Cloud Computing, Cyberattacks, Computer Programming Tools, Computational Logic, Applied Machine Learning, Calculus, Computational Thinking, Feature Engineering, Graphic Design, Investment Management, Other Programming Languages, Sales, Scrum (Software Development), Software Architecture, Software Testing, System Programming, Visual Design, Human Resources, Matlab, Artificial Neural Networks, Emotional Intelligence, Market Analysis, NoSQL, Statistical Visualization, Basic Descriptive Statistics, C Programming Language Family, Data Analysis Software, Exploratory Data Analysis, FinTech, Material Handling, Organizational Development, Product Lifecycle, Risk Management, Strategy, Amazon Web Services, Big Data, Cloud Platforms, Culture, Decision Making, Graphics Software, Microarchitecture, Security Strategy, Application Development, Computer Security Models, Leadership Development, Operational Analysis, Product Design, Reinforcement Learning, Software Security, System Security, User Research, Market Research, Plot (Graphics), R Programming, Account Management, Banking, BlockChain, Business Process Management, Contract Management, Data Architecture, Financial Accounting, Financial Management, Generally Accepted Accounting Principles (GAAP), Geovisualization, Inventory Management, Management Accounting, Markov Model, Natural Language Processing, Operations Management, Planning, Product Management, Spreadsheet Software, Storytelling, Supplier Relationship Management, Benefits, Collaboration, Computer Science, Computer Security Incident Management, Data Science, Dimensionality Reduction, Forecasting, Journalism, Negotiation, Network Analysis, Network Security, Resilience, System Software
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University of Colorado Boulder
Master of Science in Electrical Engineering
Habilidades que obtendrás: Theoretical Computer Science, Probability & Statistics, General Statistics, Algorithms, Data Management, Strategy and Operations, Computer Architecture, Mathematics, Leadership and Management, Databases, Data Analysis, Communication, Hardware Design, Operating Systems, Statistical Programming, Computer Programming, Machine Learning, Research and Design, Business Analysis, Finance, Regression, SQL, Writing, Project Management, Data Structures, Statistical Analysis, Business Communication, Experiment, Entrepreneurship, Probability Distribution, Software Engineering, Database Design, Statistical Tests, Computer Networking, Accounting, Computer Graphics, Design and Product, Systems Design, Data Visualization, Data Model, Database Administration, Database Application, Database Theory, Machine Learning Algorithms, Operations Research, Statistical Machine Learning, Human Computer Interaction, Estimation, Internet Of Things, PostgreSQL, Problem Solving, Network Architecture, Graph Theory, Applied Mathematics, Computer Vision, Deep Learning, Programming Principles, Geometry, User Experience, Econometrics, Marketing, Mathematical Theory & Analysis, Python Programming, Supply Chain and Logistics, Computer Graphic Techniques, Algebra, Cryptography, Business Psychology, Interactive Design, Budget Management, Correlation And Dependence, Distributed Computing Architecture, Financial Analysis, Linear Algebra, Linux, Critical Thinking, Security Engineering, Data Mining, Cost Accounting, Differential Equations, User Experience Design, Data Warehousing, Network Model, Cloud Computing, Cyberattacks, Computer Programming Tools, Computational Logic, Applied Machine Learning, Calculus, Computational Thinking, Feature Engineering, Graphic Design, Investment Management, Other Programming Languages, Sales, Scrum (Software Development), Software Architecture, Software Testing, System Programming, Visual Design, Human Resources, Matlab, Artificial Neural Networks, Emotional Intelligence, Market Analysis, NoSQL, Statistical Visualization, Basic Descriptive Statistics, C Programming Language Family, Data Analysis Software, Exploratory Data Analysis, FinTech, Material Handling, Organizational Development, Product Lifecycle, Risk Management, Strategy, Amazon Web Services, Big Data, Cloud Platforms, Culture, Decision Making, Graphics Software, Microarchitecture, Security Strategy, Application Development, Computer Security Models, Leadership Development, Operational Analysis, Product Design, Reinforcement Learning, Software Security, System Security, User Research, Market Research, Plot (Graphics), R Programming, Account Management, Banking, BlockChain, Business Process Management, Contract Management, Data Architecture, Financial Accounting, Financial Management, Generally Accepted Accounting Principles (GAAP), Geovisualization, Inventory Management, Management Accounting, Markov Model, Natural Language Processing, Operations Management, Planning, Product Management, Spreadsheet Software, Storytelling, Supplier Relationship Management, Benefits, Collaboration, Computer Science, Computer Security Incident Management, Data Science, Dimensionality Reduction, Forecasting, Journalism, Negotiation, Network Analysis, Network Security, Resilience, System Software
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En síntesis, estos son los 10 cursos más populares neural networks

Master of Science in Machine Learning and Data Science: Imperial College London
Medical Neuroscience: Duke 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
Philosophy, Science and Religion: Philosophy and Religion: The University of Edinburgh
Business Implications of AI: Full course: EIT Digital
Fundamentals of Machine Learning in Finance: New York University
Advanced Study of Protection Schemes and Switchgear: L&T EduTech
Introduction to Applied Machine Learning: Alberta Machine Intelligence Institute

Habilidades que puedes aprender en Machine Learning

Programación Python (33)

TensorFlow (32)

Aprendizaje Profundo (30)

Red Neuronal Artificial (24)

Macrodatos (18)

Clasificación Estadística (17)

Aprendizaje por refuerzo (13)

Preguntas frecuentes sobre Redes neuronales

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.‎

Se puso a disposición este contenido de preguntas frecuentes solo con fines informativos. Se les pide a los estudiantes que realicen investigaciones adicionales para asegurarse de que los cursos y otras credenciales cumplan con sus objetivos personales, profesionales y financieros.