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    • Recurrent Neural Network

    Recurrent Neural Network Courses Online

    Master recurrent neural networks (RNN) for sequence modeling. Learn to build and train RNNs for tasks like language modeling and time series prediction.

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    Explore the Recurrent Neural Network Course Catalog

    • D

      DeepLearning.AI

      Deep Learning

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

      Build toward a degree

      4.8
      Rating, 4.8 out of 5 stars
      ·
      146K reviews

      Intermediate · Specialization · 3 - 6 Months

    • D

      DeepLearning.AI

      Sequence Models

      Skills you'll gain: Natural Language Processing, Artificial Neural Networks, Tensorflow, Large Language Modeling, Artificial Intelligence and Machine Learning (AI/ML), PyTorch (Machine Learning Library), Deep Learning, Applied Machine Learning

      4.8
      Rating, 4.8 out of 5 stars
      ·
      31K reviews

      Intermediate · Course · 1 - 4 Weeks

    • I

      IBM

      Introduction to Neural Networks and PyTorch

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

      4.4
      Rating, 4.4 out of 5 stars
      ·
      1.8K reviews

      Intermediate · Course · 1 - 3 Months

    • D

      DeepLearning.AI

      Sequences, Time Series and Prediction

      Skills you'll gain: Tensorflow, Time Series Analysis and Forecasting, Applied Machine Learning, Deep Learning, Predictive Modeling, Artificial Neural Networks, Forecasting, Data Processing

      4.7
      Rating, 4.7 out of 5 stars
      ·
      5.1K reviews

      Intermediate · Course · 1 - 4 Weeks

    • I

      IBM

      Introduction to Deep Learning & Neural Networks with Keras

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

      4.7
      Rating, 4.7 out of 5 stars
      ·
      1.8K reviews

      Intermediate · Course · 1 - 3 Months

    • Status: Free
      Free
      S

      Sungkyunkwan University

      Fundamentals of CNNs and RNNs

      Skills you'll gain: Image Analysis, Computer Vision, Artificial Neural Networks, Natural Language Processing, Deep Learning, Network Model, Network Architecture

      4.4
      Rating, 4.4 out of 5 stars
      ·
      34 reviews

      Beginner · Course · 1 - 3 Months

    What brings you to Coursera today?

    • Status: Free
      Free
      C

      Coursera Project Network

      Deep Learning with PyTorch : Generative Adversarial Network

      Skills you'll gain: Generative AI, PyTorch (Machine Learning Library), Deep Learning, Artificial Neural Networks, Computer Vision, Applied Machine Learning

      4.5
      Rating, 4.5 out of 5 stars
      ·
      87 reviews

      Intermediate · Guided Project · Less Than 2 Hours

    • Status: New
      New
      J

      Johns Hopkins University

      Foundations of Neural Networks

      Skills you'll gain: 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), Ethical Standards And Conduct, Applied Machine Learning, Unstructured Data, Linear Algebra, Markov Model, Data-Driven Decision-Making, Natural Language Processing

      Intermediate · Specialization · 3 - 6 Months

    • Status: AI skills
      AI skills
      I

      IBM

      IBM Data Science

      Skills you'll gain: Dashboard, Data Visualization Software, Data Wrangling, Data Visualization, SQL, Supervised Learning, Feature Engineering, Plotly, Interactive Data Visualization, Jupyter, Data Literacy, Exploratory Data Analysis, Data Mining, Data Cleansing, Matplotlib, Data Analysis, Unsupervised Learning, Generative AI, Pandas (Python Package), Professional Networking

      Build toward a degree

      4.6
      Rating, 4.6 out of 5 stars
      ·
      142K reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • D

      DeepLearning.AI

      Neural Networks and Deep Learning

      Skills you'll gain: Deep Learning, Artificial Neural Networks, Supervised Learning, Artificial Intelligence, Computer Vision, Machine Learning, Python Programming, Performance Tuning

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

      Intermediate · Course · 1 - 4 Weeks

    • I

      IBM

      IBM AI Engineering

      Skills you'll gain: Prompt Engineering, Large Language Modeling, PyTorch (Machine Learning Library), Supervised Learning, Feature Engineering, Generative AI, Keras (Neural Network Library), Deep Learning, Jupyter, Natural Language Processing, Reinforcement Learning, Unsupervised Learning, Generative AI Agents, Scikit Learn (Machine Learning Library), Image Analysis, Data Manipulation, Tensorflow, Python Programming, Verification And Validation, Artificial Neural Networks

      Build toward a degree

      4.6
      Rating, 4.6 out of 5 stars
      ·
      20K reviews

      Intermediate · Professional Certificate · 3 - 6 Months

    • I

      IBM

      IBM Cybersecurity Analyst

      Skills you'll gain: Cyber Threat Hunting, Incident Response, IT Service Management, Identity and Access Management, Cloud Computing, Information Technology Infrastructure Library, Endpoint Security, Malware Protection, Penetration Testing, Linux, Computer Security Incident Management, Database Management, Network Security, Networking Hardware, TCP/IP, Cybersecurity, Intrusion Detection and Prevention, Cyber Threat Intelligence, Interviewing Skills, Cyber Security Assessment

      Build toward a degree

      4.6
      Rating, 4.6 out of 5 stars
      ·
      26K reviews

      Beginner · Professional Certificate · 3 - 6 Months

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

    • Deep Learning: DeepLearning.AI
    • Sequence Models: DeepLearning.AI
    • Introduction to Neural Networks and PyTorch: IBM
    • Sequences, Time Series and Prediction: DeepLearning.AI
    • Introduction to Deep Learning & Neural Networks with Keras: IBM
    • Fundamentals of CNNs and RNNs: Sungkyunkwan University
    • Deep Learning with PyTorch : Generative Adversarial Network: Coursera Project Network
    • Foundations of Neural Networks: Johns Hopkins University
    • IBM Data Science: IBM
    • Neural Networks and Deep Learning: DeepLearning.AI

    Frequently Asked Questions about Recurrent Neural Network

    A recurrent neural network is a type of computer network that follows a sequence of logic to reach a conclusion. It's designed to mimic the thought process of the human brain. Recurrent neural networks can be used to classify data to build sequences, time series, and predictions. They're a component of artificial intelligence and machine learning.‎

    It's important to learn about recurrent neural networks since they have many uses in computer programming. In particular, you can use deep learning and artificial intelligence to produce predictive results used to solve scientific and business problems ranging from identifying new drug compounds to building sales forecasts. You can utilize recurrent neural networks to identify emotions in tweets, analyze business data, or develop statistics. Recurrent neural networks can help you to manage data problems such as natural language processing, correcting data errors, handling missing entries, and extracting information from small data sets. The technology has a great deal of power, so people who know how to use it properly are in demand.‎

    Online courses on Coursera can help you learn about recurrent neural networks in several languages and for many different uses. Classes cover TensorFlow, Keras, and natural language processing. Some emphasize theories. Others look at recurrent neural networks as part of other computer science disciplines like artificial intelligence or deep learning. Courses are offered at beginner, intermediate, and advanced levels. Most courses have projects to apply what you learn. Some lead to Professional Certificates and Specializations. Guided Projects are offered to help you demonstrate your current level of understanding.‎

    Before starting to learn about recurrent neural networks, it is helpful to have a basic understanding of computers and programming. Even at a beginner level, some courses assume that you understand the basics of Python. Some courses offer a general introduction to artificial intelligence and machine learning, including the concepts of recurrent neural networks, and do not call for programming or computing skills.‎

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

    When looking to enhance your workforce's skills in Recurrent Neural Network, 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.‎

    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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