We may earn an affiliate commission when you visit our partners.
Mat Leonard
This course is a part of the Deep Learning Foundations Nanodegree Program.
Read more
This course is a part of the Deep Learning Foundations Nanodegree Program.
Read more
This course is a part of the Deep Learning Foundations Nanodegree Program.
Recurrent neural networks learn from sequences of data, like words in text, stock prices, or audio for music generation. Here you'll learn how to build and train recurrent neural networks in TensorFlow, then use them to generate text word by word.
Register for this course and see more details by visiting:
OpenCourser.com/course/vrywd3/udacity
Two deals to help you save
We found two deals and offers that may be relevant to this course.
Save money when you learn.
All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.
Use code at checkout.
Valid for a limited time only
Bootcamp level quality
Complete hands-on projects and get personalized feedback with Udacity.
Take
40%
off
UDACITY40
Valid for a limited time only
Future-proof your career
Access O'Reilly books, live events, courses, and more. Save with an annual subscription.
Take
15%
off
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores fundamental Deep Learning concepts in a structured manner, which is standard in industry
Teaches essential Deep Learning methods such as Recurrent Neural Networks (RNNs), which is highly relevant to various fields
Develops practical skills in building and training RNN models in TensorFlow, which is valuable for professional growth
Taught by experienced instructors from industry, ensuring practical relevance
Forms part of a larger Nanodegree program, indicating comprehensiveness
Involves hands-on projects and interactive materials, promoting experiential learning
Save this course
Save Deep Learning - Recurrent Neural Networks to your list so you can find it easily later:
Save
Save
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Deep Learning - Recurrent Neural Networks with these
activities:
Review prerequisites
Show steps
Start the course on a strong foundation. Review the required prerequisites to ensure you are ready.
Browse courses on
Python
Show steps
-
Go over the course prerequisites
-
Set aside 2 hours per week to study the prerequisites
-
Review online tutorials and documentation
-
Take practice quizzes or mock tests to assess your understanding
-
Reach out to classmates or online communities for help or clarification
Create a personal study guide
Show steps
Enhance your learning experience by creating a personalized study guide that caters to your needs.
Show steps
-
Identify the key concepts and materials from the course
-
Summarize and organize the information in your own words
-
Include diagrams, examples, and practice questions
-
Review your study guide regularly
-
Use your study guide to prepare for tests and assessments
Join a study group
Show steps
Enhance your learning through collaboration. Join a study group to discuss concepts, share ideas, and support each other.
Show steps
-
Reach out to your classmates or online communities
-
Form a study group with 3-5 members
-
Establish regular meeting times and a study schedule
-
Take turns presenting and leading discussions
-
Collaborate on projects or assignments
Four other activities
Expand to see all activities and additional details
Show all seven activities
Complete coding exercises
Show steps
Reinforce your understanding of the concepts through practice. Engage in coding exercises and drills to improve your problem-solving skills.
Browse courses on
TensorFlow
Show steps
-
Review the course materials and identify areas for practice
-
Search for online coding exercises or platforms
-
Set aside dedicated time to practice coding
-
Attempt exercises of varying difficulty levels
-
Analyze your results and identify areas for improvement
Explore additional TensorFlow resources
Show steps
Deepen your understanding of TensorFlow and its applications. Take advantage of the wealth of online resources available.
Browse courses on
TensorFlow
Show steps
-
Identify specific areas or concepts you want to explore further
-
Search for and enroll in relevant online tutorials or courses
-
Set aside dedicated time to work through the tutorials
-
Practice applying the concepts learned in your own projects
-
Engage in discussions or forums to ask questions and share knowledge
Build a text generation model
Show steps
Apply your skills to a practical project. Build a text generation model and use it to create unique and engaging text content.
Browse courses on
Recurrent Neural Networks
Show steps
-
Design the architecture of your model
-
Gather and prepare your training data
-
Train your model using TensorFlow
-
Evaluate the performance of your model
-
Use your model to generate different types of text
Develop a personal text generation application
Show steps
Take your learning to the next level. Embark on a project that allows you to apply your skills and create a meaningful application.
Browse courses on
Recurrent Neural Networks
Show steps
-
Identify a specific problem or need that your application could address
-
Design and plan the architecture of your application
-
Gather and prepare your data
-
Develop and train your model using TensorFlow
-
Integrate your model into a user-friendly interface
Review prerequisites
Show steps
Start the course on a strong foundation. Review the required prerequisites to ensure you are ready.
Browse courses on
Python
Show steps
Show steps
Show steps
- Go over the course prerequisites
- Set aside 2 hours per week to study the prerequisites
- Review online tutorials and documentation
- Take practice quizzes or mock tests to assess your understanding
- Reach out to classmates or online communities for help or clarification
Create a personal study guide
Show steps
Enhance your learning experience by creating a personalized study guide that caters to your needs.
Show steps
Show steps
Show steps
- Identify the key concepts and materials from the course
- Summarize and organize the information in your own words
- Include diagrams, examples, and practice questions
- Review your study guide regularly
- Use your study guide to prepare for tests and assessments
Join a study group
Show steps
Enhance your learning through collaboration. Join a study group to discuss concepts, share ideas, and support each other.
Show steps
Show steps
Show steps
- Reach out to your classmates or online communities
- Form a study group with 3-5 members
- Establish regular meeting times and a study schedule
- Take turns presenting and leading discussions
- Collaborate on projects or assignments
Four other activities
Expand to see all activities and additional details
Show all seven activities
Complete coding exercises
Show steps
Reinforce your understanding of the concepts through practice. Engage in coding exercises and drills to improve your problem-solving skills.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Review the course materials and identify areas for practice
- Search for online coding exercises or platforms
- Set aside dedicated time to practice coding
- Attempt exercises of varying difficulty levels
- Analyze your results and identify areas for improvement
Explore additional TensorFlow resources
Show steps
Deepen your understanding of TensorFlow and its applications. Take advantage of the wealth of online resources available.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Identify specific areas or concepts you want to explore further
- Search for and enroll in relevant online tutorials or courses
- Set aside dedicated time to work through the tutorials
- Practice applying the concepts learned in your own projects
- Engage in discussions or forums to ask questions and share knowledge
Build a text generation model
Show steps
Apply your skills to a practical project. Build a text generation model and use it to create unique and engaging text content.
Browse courses on
Recurrent Neural Networks
Show steps
Show steps
Show steps
- Design the architecture of your model
- Gather and prepare your training data
- Train your model using TensorFlow
- Evaluate the performance of your model
- Use your model to generate different types of text
Develop a personal text generation application
Show steps
Take your learning to the next level. Embark on a project that allows you to apply your skills and create a meaningful application.
Browse courses on
Recurrent Neural Networks
Show steps
Show steps
Show steps
- Identify a specific problem or need that your application could address
- Design and plan the architecture of your application
- Gather and prepare your data
- Develop and train your model using TensorFlow
- Integrate your model into a user-friendly interface
Career center
Learners who complete Deep Learning - Recurrent Neural Networks will develop knowledge and skills
that may be useful to these careers:
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers develop computer vision systems, such as facial recognition systems and object detection systems. To be successful in this role, you will need to have a strong understanding of computer vision algorithms, as well as experience with programming languages and software development tools. This course can help you build a foundation in deep learning, which is a powerful tool for computer vision. By taking this course, you will gain the skills and knowledge you need to succeed as a Computer Vision Engineer.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, and deploy machine learning models. They work on a variety of projects, including image recognition, natural language processing, and predictive analytics. To be successful in this role, you will need to have a strong understanding of machine learning algorithms, as well as experience with programming languages and software development tools. This course can help you build a foundation in deep learning, which is a powerful tool for machine learning. By taking this course, you will gain the skills and knowledge you need to succeed as a Machine Learning Engineer.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematics and statistics to solve problems in the financial industry. They work on a variety of projects, including risk management, portfolio optimization, and trading. To be successful in this role, you will need to have a strong understanding of mathematics and statistics, as well as experience with programming languages and software development tools. This course can help you build a foundation in deep learning, which is a powerful tool for quantitative analysis. By taking this course, you will gain the skills and knowledge you need to succeed as a Quantitative Analyst.
Data Scientist
Data Scientist
Data Scientists use data to solve problems and make decisions. They work in a variety of industries, including finance, healthcare, and manufacturing. To be successful in this role, you will need to have a strong understanding of statistics, machine learning, and data visualization. This course can help you build a foundation in deep learning, which is a powerful tool for data analysis and modeling. By taking this course, you will gain the skills and knowledge you need to succeed as a Data Scientist.
Research Scientist
Research Scientist
Research Scientists conduct research in a variety of fields, including computer science, biology, and physics. They develop new theories and technologies, and publish their findings in academic journals. To be successful in this role, you will need to have a strong foundation in mathematics and science, as well as experience with programming languages and software development tools. This course can help you build a foundation in deep learning, which is a powerful tool for research. By taking this course, you will gain the skills and knowledge you need to succeed as a Research Scientist.
Software Architect
Software Architect
Software Architects design and develop software systems. To be successful in this role, you will need to have a strong understanding of software engineering principles, as well as experience with programming languages and software development tools. This course can help you build a foundation in deep learning, which is a powerful tool for software architecture. By taking this course, you will gain the skills and knowledge you need to succeed as a Software Architect.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to help businesses make decisions. To be successful in this role, you will need to have a strong understanding of data analysis techniques, as well as experience with programming languages and software development tools. This course can help you build a foundation in deep learning, which is a powerful tool for data analysis. By taking this course, you will gain the skills and knowledge you need to succeed as a Data Analyst.
Systems Engineer
Systems Engineer
Systems Engineers design and develop complex systems, such as computer networks and telecommunications systems. To be successful in this role, you will need to have a strong understanding of systems engineering principles, as well as experience with programming languages and software development tools. This course can help you build a foundation in deep learning, which is a powerful tool for systems engineering. By taking this course, you will gain the skills and knowledge you need to succeed as a Systems Engineer.
Software Engineer
Software Engineer
As a Software Engineer, you will be responsible for developing, testing, and maintaining software applications. You will need to have a strong understanding of computer science fundamentals, as well as experience with programming languages and software development tools. This course can help you build a foundation in deep learning, which is a rapidly growing field that is used in a variety of applications, including software development. By taking this course, you will gain the skills and knowledge you need to succeed as a Software Engineer.
Teacher
Teacher
Teachers educate students in a variety of subjects, including computer science, math, and science. To be successful in this role, you will need to have a strong understanding of your subject matter, as well as experience with teaching and communication. This course can help you build a foundation in deep learning, which is a powerful tool for teaching. By taking this course, you will gain the skills and knowledge you need to succeed as a Teacher.
Product Manager
Product Manager
Product Managers are responsible for developing and managing products. They work with engineers, designers, and marketers to create products that meet the needs of customers. To be successful in this role, you will need to have a strong understanding of business and technology, as well as experience with product development. This course can help you build a foundation in deep learning, which is a powerful tool for product development. By taking this course, you will gain the skills and knowledge you need to succeed as a Product Manager.
Business Analyst
Business Analyst
Business Analysts use data to solve problems and make decisions in the business world. They work with stakeholders to identify business needs and develop solutions. To be successful in this role, you will need to have a strong understanding of business and data analysis, as well as experience with programming languages and software development tools. This course can help you build a foundation in deep learning, which is a powerful tool for business analysis. By taking this course, you will gain the skills and knowledge you need to succeed as a Business Analyst.
Consultant
Consultant
Consultants provide advice and guidance to businesses on a variety of topics, including strategy, operations, and technology. To be successful in this role, you will need to have a strong understanding of business and technology, as well as experience with problem-solving and communication. This course can help you build a foundation in deep learning, which is a powerful tool for consulting. By taking this course, you will gain the skills and knowledge you need to succeed as a Consultant.
Technical Writer
Technical Writer
Technical Writers create documentation for software and other technical products. To be successful in this role, you will need to have a strong understanding of technical concepts, as well as experience with writing and editing. This course can help you build a foundation in deep learning, which is a powerful tool for technical writing. By taking this course, you will gain the skills and knowledge you need to succeed as a Technical Writer.
UX Designer
UX Designer
UX Designers create user interfaces for software and other products. To be successful in this role, you will need to have a strong understanding of user experience principles, as well as experience with design tools and software development. This course can help you build a foundation in deep learning, which is a powerful tool for UX design. By taking this course, you will gain the skills and knowledge you need to succeed as a UX Designer.
For more career information including salaries, visit:
OpenCourser.com/course/vrywd3/udacity
Reading list
We've selected six books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Deep Learning - Recurrent Neural Networks.
Save
A comprehensive resource on the history, theory, and applications of recurrent neural networks. Offers in-depth insights into the mathematical foundations and practical considerations for building and training RNNs.
Save
Covers the foundational concepts of deep learning, including recurrent neural networks. Provides a comprehensive overview of the field and serves as a valuable reference for practitioners.
Provides a practical introduction to deep learning using Python. Covers the basics of recurrent neural networks and offers hands-on examples for building and training RNNs with Keras, a high-level API for TensorFlow.
Introduces the fundamentals of natural language processing and provides practical examples of using Python libraries for NLP tasks. Offers a solid foundation for understanding how recurrent neural networks are used in NLP.
Offers a practical guide to building and deploying NLP systems with Python. Covers recurrent neural networks and provides real-world examples of NLP applications.
An intuitive and accessible introduction to neural networks and deep learning. Provides a conceptual understanding of RNNs and their applications without requiring extensive mathematical background.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/vrywd3/udacity
Share
Similar courses
Here are nine courses similar to
Deep Learning - Recurrent Neural Networks.
Sentiment Analysis with Recurrent Neural Networks in TensorFlow
OpenCourser.com/course/bmuusg/sentiment
Sentiment Analysis with Recurrent Neural Networks in...
Most relevant
Natural Language Processing with PyTorch
OpenCourser.com/course/256rqv/natural
Natural Language Processing with PyTorch
Most relevant
Mastering Natural Language Processing (NLP) with Deep Learning
OpenCourser.com/course/47npfg/mastering
Mastering Natural Language Processing (NLP) with Deep...
Most relevant
Implement Natural Language Processing for Word Embedding
OpenCourser.com/course/9j6ifr/implement
Implement Natural Language Processing for Word Embedding
Most relevant
The Complete Neural Networks Bootcamp: Theory, Applications
OpenCourser.com/course/6h9wcw/the
The Complete Neural Networks Bootcamp: Theory,...
Deep Learning with Tensorflow
OpenCourser.com/course/eil4oq/deep
Deep Learning with Tensorflow
Natural Language Processing with Deep Learning in Python
OpenCourser.com/course/yuizfh/natural
Natural Language Processing with Deep Learning in Python
Using Neural Networks for Image and Voice Data Analysis
OpenCourser.com/course/veqodu/using
Using Neural Networks for Image and Voice Data Analysis
Machine Learning: Natural Language Processing in Python (V2)
OpenCourser.com/course/a78lpa/machine
Machine Learning: Natural Language Processing in Python...
Via
Udacity
Instructor
Mat Leonard
Good to know
Explores fundamental Deep Learning concepts in a structured manner, which is standard in industry
Teaches essential Deep Learning methods such as Recurrent Neural Networks (RNNs), which is highly relevant to various fields
Develops practical skills in building and training RNN models in TensorFlow, which is valuable for professional growth
Taught by experienced instructors from industry, ensuring practical relevance
Forms part of a larger Nanodegree program, indicating comprehensiveness
Involves hands-on projects and interactive materials, promoting experiential learning
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2024 OpenCourser