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Study neural networks for deep learning applications. Learn about network architectures, training algorithms, and model evaluation.

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Explore the Neural Networks Course Catalog

  • Status: Free Trial
    Free Trial
    D

    DeepLearning.AI

    Neural Networks and Deep Learning

    Skills you'll gain: Deep Learning, Artificial Neural Networks, Supervised Learning, Artificial Intelligence, Machine Learning, Python Programming, Linear Algebra, Calculus

    4.9
    Rating, 4.9 out of 5 stars
    ·
    123K reviews

    Intermediate · Course · 1 - 4 Weeks

  • Status: Free Trial
    Free Trial
    I

    IBM

    Introduction to Deep Learning & Neural Networks with Keras

    Skills you'll gain: Keras (Neural Network Library), Deep Learning, PyTorch (Machine Learning Library), Artificial Neural Networks, Tensorflow, Image Analysis, Computer Vision, Natural Language Processing, Machine Learning Algorithms, Machine Learning, Network Architecture, Regression Analysis

    4.7
    Rating, 4.7 out of 5 stars
    ·
    2K reviews

    Intermediate · Course · 1 - 3 Months

  • Status: New
    New
    Status: Free Trial
    Free Trial
    P

    Pearson

    Learning Deep Learning

    Skills you'll gain: Large Language Modeling, Deep Learning, Prompt Engineering, Image Analysis, PyTorch (Machine Learning Library), Tensorflow, LLM Application, Computer Vision, Responsible AI, Natural Language Processing, Generative AI, Artificial Neural Networks, Data Ethics, Multimodal Prompts, Artificial Intelligence and Machine Learning (AI/ML), Applied Machine Learning, Machine Learning Methods, Artificial Intelligence, Application Deployment, Time Series Analysis and Forecasting

    Intermediate · Specialization · 1 - 4 Weeks

  • Status: New
    New
    Status: Free Trial
    Free Trial
    P

    Packt

    Deep Learning with TensorFlow

    Skills you'll gain: Tensorflow, Artificial Neural Networks, Keras (Neural Network Library), Deep Learning, Time Series Analysis and Forecasting, Image Analysis, Natural Language Processing, Computer Vision, Forecasting, Classification And Regression Tree (CART), Supervised Learning, Machine Learning, Text Mining, Predictive Analytics, NumPy, Network Architecture, Data Processing, Data Science

    Intermediate · Specialization · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    J

    Johns Hopkins University

    Foundations of Neural Networks

    Skills you'll gain: Responsible AI, Data Ethics, Artificial Neural Networks, Deep Learning, Machine Learning Algorithms, Reinforcement Learning, Generative AI, Debugging, Artificial Intelligence, Unsupervised Learning, Machine Learning, Computer Vision, Image Analysis, Artificial Intelligence and Machine Learning (AI/ML), Machine Learning Methods, Applied Machine Learning, Bayesian Statistics, Network Architecture, Linear Algebra, Markov Model

    4.1
    Rating, 4.1 out of 5 stars
    ·
    7 reviews

    Intermediate · Specialization · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    D

    DeepLearning.AI

    Deep Learning

    Skills you'll gain: Computer Vision, Deep Learning, Image Analysis, Natural Language Processing, Artificial Neural Networks, Artificial Intelligence and Machine Learning (AI/ML), Tensorflow, Supervised Learning, Keras (Neural Network Library), Artificial Intelligence, Applied Machine Learning, PyTorch (Machine Learning Library), Machine Learning, Debugging, Performance Tuning, Python Programming, Data-Driven Decision-Making, Text Mining, Feature Engineering, Machine Learning Algorithms

    Build toward a degree

    4.8
    Rating, 4.8 out of 5 stars
    ·
    147K reviews

    Intermediate · Specialization · 3 - 6 Months

What brings you to Coursera today?

  • Status: Free Trial
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    IBM

    Introduction to Neural Networks and PyTorch

    Skills you'll gain: PyTorch (Machine Learning Library), Tensorflow, Artificial Neural Networks, Deep Learning, Predictive Modeling, Probability & Statistics, Machine Learning, Regression Analysis, Data Manipulation

    4.4
    Rating, 4.4 out of 5 stars
    ·
    1.9K reviews

    Intermediate · Course · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    J

    Johns Hopkins University

    Introduction to Neural Networks

    Skills you'll gain: Artificial Neural Networks, Machine Learning Algorithms, Deep Learning, Computer Vision, Image Analysis, Artificial Intelligence and Machine Learning (AI/ML), Applied Machine Learning, Machine Learning, Network Architecture, Linear Algebra, Performance Tuning, Probability & Statistics

    Intermediate · Course · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    D

    DeepLearning.AI

    Convolutional Neural Networks

    Skills you'll gain: Computer Vision, Image Analysis, Deep Learning, Artificial Neural Networks, Keras (Neural Network Library), Tensorflow, Applied Machine Learning, PyTorch (Machine Learning Library), Artificial Intelligence and Machine Learning (AI/ML), Feature Engineering, Algorithms

    4.9
    Rating, 4.9 out of 5 stars
    ·
    43K reviews

    Intermediate · Course · 1 - 4 Weeks

  • Status: Free Trial
    Free Trial
    D

    DeepLearning.AI

    Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

    Skills you'll gain: Tensorflow, Deep Learning, Artificial Intelligence and Machine Learning (AI/ML), Performance Tuning, Artificial Neural Networks, Applied Machine Learning, Supervised Learning, Machine Learning Algorithms

    4.9
    Rating, 4.9 out of 5 stars
    ·
    63K reviews

    Intermediate · Course · 1 - 4 Weeks

  • Status: Free Trial
    Free Trial
    P

    Packt

    Deep Learning: Recurrent Neural Networks with Python

    Skills you'll gain: PyTorch (Machine Learning Library), Tensorflow, Artificial Intelligence, Applied Machine Learning, Artificial Neural Networks, Deep Learning, Application Deployment, Text Mining, Machine Learning, Natural Language Processing, Predictive Modeling, Python Programming, Time Series Analysis and Forecasting, Artificial Intelligence and Machine Learning (AI/ML), Network Architecture, Performance Tuning, Data Science, Data Processing, Data Analysis

    Beginner · Specialization · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    I

    IBM

    Deep Learning with PyTorch

    Skills you'll gain: PyTorch (Machine Learning Library), Deep Learning, Artificial Neural Networks, Computer Vision, Supervised Learning, Machine Learning, Network Architecture

    4.4
    Rating, 4.4 out of 5 stars
    ·
    69 reviews

    Intermediate · Course · 1 - 3 Months

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

  • Neural Networks and Deep Learning: DeepLearning.AI
  • Introduction to Deep Learning & Neural Networks with Keras: IBM
  • Learning Deep Learning: Pearson
  • Deep Learning with TensorFlow: Packt
  • Foundations of Neural Networks: Johns Hopkins University
  • Deep Learning: DeepLearning.AI
  • Introduction to Neural Networks and PyTorch: IBM
  • Introduction to Neural Networks: Johns Hopkins University
  • Convolutional Neural Networks: DeepLearning.AI
  • Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization: DeepLearning.AI

Skills you can learn in Machine Learning

Python Programming (33)
Tensorflow (32)
Deep Learning (30)
Artificial Neural Network (24)
Big Data (18)
Statistical Classification (17)
Reinforcement Learning (13)
Algebra (10)
Bayesian (10)
Linear Algebra (10)
Linear Regression (9)
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.‎

Online Neural Networks courses offer a convenient and flexible way to enhance your knowledge or learn new Neural Networks skills. Choose from a wide range of Neural Networks courses offered by top universities and industry leaders tailored to various skill levels.‎

When looking to enhance your workforce's skills in Neural Networks, it's crucial to select a course that aligns with their current abilities and learning objectives. Our Skills Dashboard is an invaluable tool for identifying skill gaps and choosing the most appropriate course for effective upskilling. For a comprehensive understanding of how our courses can benefit your employees, explore the enterprise solutions we offer. Discover more about our tailored programs at Coursera for Business here.‎

Graph neural networks (GNNs) are a type of deep learning model designed to process data structured as graphs, such as social networks, molecular structures, or recommendation systems. GNNs learn relationships between nodes and edges to make predictions or classifications. Courses like Deep Learning Specialization from DeepLearning.AI on Coursera provide an in-depth introduction to GNNs and their applications.‎

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.

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