We may earn an affiliate commission when you visit our partners.
Jay Alammar,
Arpan Chakraborty,
Luis Serrano,
and
Dana Sheahen
Register for this course and see more details by visiting:
OpenCourser.com/course/3oathq/recurrent
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
What's inside
Syllabus
Ortal will introduce Recurrent Neural Networks (RNNs), which are machine learning models that are able to recognize and act on sequences of inputs.
Luis explains Long Short-Term Memory Networks (LSTM), and similar architectures which have the benefits of preserving long term memory.
Learn about a number of different hyperparameters that are used in defining and training deep learning models. We'll discuss starting values and intuitions for tuning each hyperparameter.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Examines RNNs, LSTMs which are standard in AI
Taught by Jay Alammar, Arpan Chakraborty, Luis Serrano, Dana Sheahen, who are recognized for their work in AI
Builds a strong foundation for beginners in deep learning
Develops a number of different hyperparameters for defining and training deep learning models
May require additional knowledge in deep learning depending on learner's background
Save this course
Save Recurrent Neural Networks (NLP Elective) 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 Recurrent Neural Networks (NLP Elective) with these
activities:
Review prerequisites for deep learning
Show steps
Refreshing your knowledge of prerequisites will strengthen your foundation and enhance your understanding of deep learning concepts.
Browse courses on
Linear Algebra
Show steps
-
Identify the key prerequisite topics.
-
Review textbooks, online resources, or take refresher courses.
-
Solve practice problems and exercises.
-
Consider taking a diagnostic test to assess your understanding.
Read 'Deep Learning with Python' by François Chollet
Show steps
'Deep Learning with Python' provides a comprehensive overview of deep learning concepts and their application in Python.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
-
Obtain a copy of the book.
-
Read the chapters relevant to the course material.
-
Take notes and highlight important concepts.
-
Complete the exercises and practice problems.
Create a comprehensive study guide
Show steps
Compiling a comprehensive study guide will help you organize and reinforce your understanding of course materials.
Show steps
-
Gather all relevant course materials, including lecture notes, assignments, and quizzes.
-
Review and summarize the key concepts and ideas.
-
Organize the information into a logical structure, such as chapters or sections.
-
Add helpful resources, such as examples, definitions, and diagrams.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Participate in online study groups
Show steps
Engaging in online study groups will foster collaboration, enhance understanding, and improve retention.
Browse courses on
Recurrent Neural Networks (RNNs)
Show steps
-
Identify online platforms or forums for study groups.
-
Join a study group that aligns with your learning goals.
-
Actively participate in discussions, ask questions, and share insights.
-
Collaborate on projects or exercises.
Follow tutorials on hyperparameter tuning
Show steps
Hyperparameter tuning is crucial for optimizing deep learning models. Guided tutorials will provide valuable insights.
Browse courses on
Hyperparameter Tuning
Show steps
-
Search for tutorials on hyperparameter tuning for deep learning models.
-
Select a tutorial that aligns with your learning style and technical level.
-
Follow the tutorial steps and apply the techniques to your own models.
-
Experiment with different hyperparameter settings and observe the impact on model performance.
Practice with LSTM exercises
Show steps
Solving LSTM exercises will strengthen your understanding of LSTM models and their application.
Browse courses on
Recurrent Neural Networks (RNNs)
Show steps
-
Find online LSTM exercises or create your own dataset.
-
Implement LSTM models in your preferred programming language.
-
Train and evaluate your models on the exercises.
-
Analyze the results and refine your models.
Develop a text classification project using RNNs
Show steps
Building a text classification project will provide hands-on experience with RNNs and enhance your understanding.
Browse courses on
Recurrent Neural Networks (RNNs)
Show steps
-
Define the problem and gather a labeled text dataset.
-
Choose an RNN architecture and implement it in your preferred programming language.
-
Train and evaluate your model on the dataset.
-
Analyze the results and improve your model.
-
Deploy your model and evaluate its performance in a real-world scenario.
Write blog posts or articles on deep learning applications
Show steps
Writing blog posts or articles will deepen your understanding of deep learning concepts and their practical applications.
Browse courses on
Recurrent Neural Networks (RNNs)
Show steps
-
Choose a specific deep learning application or technique.
-
Research the topic thoroughly.
-
Write a clear and concise blog post or article.
-
Share your writing with others and engage in discussions.
Review prerequisites for deep learning
Show steps
Refreshing your knowledge of prerequisites will strengthen your foundation and enhance your understanding of deep learning concepts.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Identify the key prerequisite topics.
- Review textbooks, online resources, or take refresher courses.
- Solve practice problems and exercises.
- Consider taking a diagnostic test to assess your understanding.
Read 'Deep Learning with Python' by François Chollet
Show steps
'Deep Learning with Python' provides a comprehensive overview of deep learning concepts and their application in Python.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
Show steps
Show steps
- Obtain a copy of the book.
- Read the chapters relevant to the course material.
- Take notes and highlight important concepts.
- Complete the exercises and practice problems.
Create a comprehensive study guide
Show steps
Compiling a comprehensive study guide will help you organize and reinforce your understanding of course materials.
Show steps
Show steps
Show steps
- Gather all relevant course materials, including lecture notes, assignments, and quizzes.
- Review and summarize the key concepts and ideas.
- Organize the information into a logical structure, such as chapters or sections.
- Add helpful resources, such as examples, definitions, and diagrams.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Participate in online study groups
Show steps
Engaging in online study groups will foster collaboration, enhance understanding, and improve retention.
Browse courses on
Recurrent Neural Networks (RNNs)
Show steps
Show steps
Show steps
- Identify online platforms or forums for study groups.
- Join a study group that aligns with your learning goals.
- Actively participate in discussions, ask questions, and share insights.
- Collaborate on projects or exercises.
Follow tutorials on hyperparameter tuning
Show steps
Hyperparameter tuning is crucial for optimizing deep learning models. Guided tutorials will provide valuable insights.
Browse courses on
Hyperparameter Tuning
Show steps
Show steps
Show steps
- Search for tutorials on hyperparameter tuning for deep learning models.
- Select a tutorial that aligns with your learning style and technical level.
- Follow the tutorial steps and apply the techniques to your own models.
- Experiment with different hyperparameter settings and observe the impact on model performance.
Practice with LSTM exercises
Show steps
Solving LSTM exercises will strengthen your understanding of LSTM models and their application.
Browse courses on
Recurrent Neural Networks (RNNs)
Show steps
Show steps
Show steps
- Find online LSTM exercises or create your own dataset.
- Implement LSTM models in your preferred programming language.
- Train and evaluate your models on the exercises.
- Analyze the results and refine your models.
Develop a text classification project using RNNs
Show steps
Building a text classification project will provide hands-on experience with RNNs and enhance your understanding.
Browse courses on
Recurrent Neural Networks (RNNs)
Show steps
Show steps
Show steps
- Define the problem and gather a labeled text dataset.
- Choose an RNN architecture and implement it in your preferred programming language.
- Train and evaluate your model on the dataset.
- Analyze the results and improve your model.
- Deploy your model and evaluate its performance in a real-world scenario.
Write blog posts or articles on deep learning applications
Show steps
Writing blog posts or articles will deepen your understanding of deep learning concepts and their practical applications.
Browse courses on
Recurrent Neural Networks (RNNs)
Show steps
Show steps
Show steps
- Choose a specific deep learning application or technique.
- Research the topic thoroughly.
- Write a clear and concise blog post or article.
- Share your writing with others and engage in discussions.
Career center
Learners who complete Recurrent Neural Networks (NLP Elective) will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers can build deep learning models to understand natural language data. This course will introduce recurrent neural networks (RNNs), the foundation of modern natural language processing (NLP) models. The course also covers Long Short-Term Memory (LSTM) Networks, which help preserve long term memory. This course will help you develop the skills needed to understand and build cutting-edge NLP models.
Data Scientist
Data Scientist
Data Scientists use various machine learning and deep learning techniques to analyze data, including NLP. Understanding RNNs will become crucial as large language models continue to grow and be integrated into more products. This course will help you understand the core concepts behind recurrent neural networks, including LSTMs. Additionally, this course will help you build a foundation in understanding how to develop, train, and tune deep learning models.
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers are responsible for developing and maintaining NLP models. This course will introduce RNNs and LSTMs, the foundation of modern NLP. This course is an excellent opportunity to learn how to manipulate and sequence data.
Software Engineer
Software Engineer
Software Engineers can benefit from taking this course to gain a deeper understanding of RNNs, the foundation of modern NLP models, which can be integrated into their software products.
Research Scientist
Research Scientist
Research Scientists may take this course to enhance their understanding of RNNs, particularly LSTMs, for use in their research. The course will be useful in building a foundation of deep learning techniques for NLP.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts may find this course helpful in understanding how to use RNNs, especially LSTMs, to build financial models.
Business Analyst
Business Analyst
Business Analysts may find this course helpful to understand how to use RNNs, notably LSTMs, to build NLP models for business applications.
Product Manager
Product Manager
Product Managers may find this course helpful to gain a basic understanding of RNNs and LSTMs, which can be used to build NLP features into their products.
Data Analyst
Data Analyst
Data Analysts may find this course helpful to gain a basic understanding of RNNs and LSTMs, which can be used to analyze NLP data.
Technical Writer
Technical Writer
Technical Writers may find this course helpful to gain a basic understanding of RNNs and LSTMs, which can be used to write documentation for NLP systems.
User Experience Designer
User Experience Designer
User Experience Designers may find this course helpful to understand how NLP models can be used to improve the user experience.
Marketing Manager
Marketing Manager
Marketing Managers may find this course helpful to gain a basic understanding of RNNs and LSTMs, which can be used to analyze NLP data for marketing campaigns.
Sales Manager
Sales Manager
Sales Managers may find this course helpful to gain a basic understanding of RNNs and LSTMs, which can be used to analyze NLP data for sales.
Customer Success Manager
Customer Success Manager
Customer Success Managers may find this course helpful to gain a basic understanding of RNNs and LSTMs, which can be used to analyze NLP data for customer feedback.
IT Manager
IT Manager
IT Managers may find this course helpful to gain a basic understanding of RNNs and LSTMs, which can be used to manage NLP systems.
For more career information including salaries, visit:
OpenCourser.com/course/3oathq/recurrent
Reading list
We've selected nine 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
Recurrent Neural Networks (NLP Elective).
Comprehensive textbook on statistical learning methods, including linear regression, logistic regression, and tree-based methods. Provides a solid theoretical foundation for understanding the statistical principles underlying machine learning models.
Theoretical treatment of machine learning concepts and algorithms. Provides a rigorous mathematical foundation for understanding the principles underlying machine learning models.
Save
Comprehensive textbook covering deep learning concepts and techniques, including hyperparameter tuning and optimization. Provides a solid foundation for understanding the underlying principles of deep learning models.
Save
Classic textbook on machine learning concepts and algorithms. Provides a solid foundation for understanding the theoretical principles and practical techniques used in machine learning.
Practical guide to NLP techniques and tools. Focuses on hands-on implementation of NLP projects using Python libraries. Provides a solid understanding of the practical aspects of NLP.
Practical guide to machine learning using Python. Covers a wide range of topics, including data preprocessing, feature engineering, model selection, and evaluation. Focuses on hands-on implementation and real-world applications.
Practical guide to machine learning using popular Python libraries. Covers data preprocessing, feature engineering, model selection, and evaluation. Focuses on hands-on implementation and real-world applications.
Practical guide to deep learning using the Keras library in Python. Covers a wide range of topics, including neural network architectures, training techniques, and evaluating models. Focuses on hands-on implementation.
Comprehensive textbook on speech and language processing, covering topics such as speech recognition, natural language understanding, and dialogue systems. Provides a solid foundation for understanding the principles and techniques used in NLP applications.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/3oathq/recurrent
Share
Similar courses
Similar courses are unavailable at this time. Please try again later.
Time
280 hours
Level
Advanced
Via
Udacity
Instructors
Jay Alammar
Arpan Chakraborty
Luis Serrano
Dana Sheahen
Language
English
Good to know
Examines RNNs, LSTMs which are standard in AI
Taught by Jay Alammar, Arpan Chakraborty, Luis Serrano, Dana Sheahen, who are recognized for their work in AI
Builds a strong foundation for beginners in deep learning
Develops a number of different hyperparameters for defining and training deep learning models
May require additional knowledge in deep learning depending on learner's background
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