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Learn about neural networks with online courses and programs

Contribute to cutting-edge technologies that are transforming sectors like healthcare and finance. Learn how to design, train, and optimize neural networks with edX.

What are neural networks?

Neural networks are a foundational concept in the field of artificial intelligence (AI) and machine learning. They are computational models inspired by the structure and functioning of the human brain. These networks consist of interconnected nodes, or "neurons," that process and transmit information, allowing machines to learn patterns, recognize complex relationships, and make predictions from data.

Neural networks excel in tasks like image and speech recognition, natural language processing, recommendation systems, and even playing games. They have contributed to breakthroughs in autonomous vehicles, medical diagnoses, and understanding intricate data patterns.

Neural Network | Introduction Image

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Neural networks course curriculum

A course on neural networks could provide learners with a deep understanding of the concepts, principles, and techniques behind these AI models. From fundamental theory to practical applications, there are a wide range of topics available to cover. Some examples of content learners might encounter include:

  • Basics of neural network architecture

  • Types of neural networks, such as CNNs and RNNs

  • Activation functions

  • Loss and cost functions to measure accuracy

  • Backpropagation algorithms

Start building the knowledge you need to work in the artificial intelligence field with edX. From accelerated boot camps to comprehensive programs that allow you to earn a bachelor’s degree or (for more advanced learners) a master’s degree, there are many different learning formats available to fit your needs. Busy professionals can even take advantage of executive education courses. Find the right course for you.

Explore jobs related to neural networks

Learning about neural networks could be applicable to a variety of careers in artificial intelligence, machine learning, and data science. Some examples of potential jobs include:

  • Machine learning engineer: Develop and implement machine learning models, including neural networks, to solve complex problems and create AI-powered applications.

  • Data scientist: Analyze and interpret data using neural networks to extract insights, make predictions, and drive informed decision making.

  • Computer vision engineer: Develop computer vision systems that enable machines to understand and interpret visual information, such as in autonomous vehicles and facial recognition.

  • Natural language processing (NLP) engineer: Work on projects involving language understanding, sentiment analysis, chatbots, and machine translation.

Each of these roles will have different education and skills requirements. For example, you may be able to build relevant skills in an artificial intelligence boot camp. However, some employers may seek candidates with a degree in computer science, depending on the role. Before deciding on a specific learning path, research the positions you hope to pursue and align your coursework with your career goals.