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Laurence Moroney
If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning.
Read more
If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning.
Read more
If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning.
In this fourth course, you will learn how to build time series models in TensorFlow. You’ll first implement best practices to prepare time series data. You’ll also explore how RNNs and 1D ConvNets can be used for prediction. Finally, you’ll apply everything you’ve learned throughout the Specialization to build a sunspot prediction model using real-world data!
The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.
Register for this course and see more details by visiting:
OpenCourser.com/course/1nqwix/sequences
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What's inside
Syllabus
Sequences and Prediction
Hi Learners and welcome to this course on sequences and prediction! In this course we'll take a look at some of the unique considerations involved when handling sequential time series data -- where values change over time, like the temperature on a particular day, or the number of visitors to your web site. We'll discuss various methodologies for predicting future values in these time series, building on what you've learned in previous courses!
Read more
Syllabus
Sequences and Prediction
Hi Learners and welcome to this course on sequences and prediction! In this course we'll take a look at some of the unique considerations involved when handling sequential time series data -- where values change over time, like the temperature on a particular day, or the number of visitors to your web site. We'll discuss various methodologies for predicting future values in these time series, building on what you've learned in previous courses!
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Designed for Software Developers Familiar with Machine Learning
Provides Hands-on Labs and Interactive Materials
Develops Professional Skills in Time Series Modeling
Taught by Recognized Experts in Machine Learning and TensorFlow
Requires Extensive Background Knowledge in Machine Learning
Not Suitable for Beginners in Machine Learning
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Reviews summary
Building time series models with tensorflow
According to learners, this course is a strong introduction to building time series models using TensorFlow, focusing on best practices and various model types. Students particularly appreciate the practical application and hands-on projects, which help solidify theoretical concepts. The content on RNNs and 1D ConvNets for prediction is highlighted as valuable. While many find the lectures clear and engaging, some note that the course assumes prior knowledge from previous courses in the specialization, and learners without a solid foundation in deep learning might find parts challenging. Overall, it's seen as a valuable addition to the specialization, providing practical skills for real-world problems, though some felt the real-world data section could be more in-depth.
Applies concepts to a real-world sunspot dataset.
"Applying everything to the real-world sunspot data was a great way to consolidate learning."
"I liked using a real dataset, even if it was relatively simple."
"The final project on sunspot prediction made the concepts feel more tangible."
"Working with real-world data, though simple, added practical value."
Covers RNNs, ConvNets for sequence prediction.
"The sections on RNNs and 1D ConvNets were particularly insightful for understanding how to handle sequential data."
"I appreciated learning about the different neural network architectures applicable to time series."
"It gives a solid overview of using deep learning models for prediction problems involving sequences."
"Exploring both RNNs and ConvNets provided a well-rounded perspective on modelling."
Well-explained topics, easy to follow presentation.
"The way the concepts were explained made it easy to grasp complex ideas about time series and deep learning."
"Lectures are clear and build logically on previous material."
"I found the video content very helpful in understanding the different model architectures."
"The instructor does a good job breaking down the steps for preparing data and building models."
Hands-on application of TensorFlow for time series.
"This course helped me practically apply TensorFlow to time series data for prediction tasks."
"I learned how to implement different models like RNNs and ConvNets in TensorFlow for sequences."
"The labs provided a great opportunity to code and see the models in action."
"It's great to finally see how to use TensorFlow specifically for predicting sequences."
Some felt the real-world section was brief.
"While the sunspot example was good, I wished there was a more in-depth section on applying models to more complex real-world time series."
"Could have used more examples or case studies with varied real-world data challenges."
"I felt the 'Real-world time series data' week could have been expanded significantly."
"More advanced topics or handling messy real-world data could improve the course."
Assumes background in ML/DL and prior courses.
"It's definitely important to have taken the previous courses or have a solid understanding of deep learning before starting this one."
"I struggled a bit because I didn't have a strong prior background in neural networks; it assumes you know that already."
"This course is not for absolute beginners in deep learning; it builds on prior concepts."
"Learners should be comfortable with TensorFlow basics before tackling this course."
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 Sequences, Time Series and Prediction with these
activities:
Review matrix computations
Show steps
Reinforces foundational linear algebra knowledge that is essential for understanding deep learning models.
Browse courses on
Matrix Computations
Show steps
-
Go through your notes from a previous linear algebra course or textbook.
-
Solve practice problems on matrix operations, such as addition, subtraction, multiplication, and inversion.
-
Use an online matrix calculator to check your answers and gain a better understanding of matrix operations.
Read 'Deep Learning' by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Show steps
Provides a comprehensive overview of deep learning theory and techniques, complementing the course material and offering a deeper understanding of the field.
View
Deep Learning
on Amazon
Show steps
-
Acquire a copy of the book 'Deep Learning' by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
-
Set aside dedicated time for reading and studying the book.
-
Take notes and highlight important concepts and techniques.
-
Work through the exercises and examples provided in the book to reinforce your understanding.
Practice building and training simple neural networks in TensorFlow
Show steps
Provides hands-on experience in implementing and training neural networks, which is crucial for understanding the practical aspects of deep learning.
Browse courses on
TensorFlow
Show steps
-
Follow tutorials or online courses on building neural networks in TensorFlow.
-
Create a simple neural network model for a classification or regression task.
-
Train and evaluate your model on a dataset.
-
Experiment with different hyperparameters to optimize the performance of your model.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Form a study group with classmates to discuss course material and work on projects
Show steps
Fosters collaboration, enhances understanding through peer-to-peer learning, and provides support for completing projects.
Show steps
-
Identify a few classmates who are interested in forming a study group.
-
Set regular meeting times and locations that work for everyone in the group.
-
Create a schedule for reviewing course material, discussing concepts, and working on projects together.
-
Take turns leading discussions and presenting on specific topics.
Follow online tutorials on specific TensorFlow libraries or tools
Show steps
Provides hands-on experience with specific TensorFlow libraries and tools, enhancing students' practical knowledge and ability to use them effectively.
Browse courses on
TensorFlow
Show steps
-
Identify a specific TensorFlow library or tool that you want to learn more about.
-
Search for and select reputable online tutorials that cover the library or tool.
-
Follow the tutorials step-by-step, practicing the techniques and concepts being taught.
-
Experiment with the library or tool on your own to further explore its capabilities.
Attend a workshop on advanced TensorFlow techniques
Show steps
Provides exposure to cutting-edge TensorFlow techniques and best practices, which can significantly enhance students' understanding and skills.
Browse courses on
TensorFlow
Show steps
-
Identify and register for a workshop on advanced TensorFlow techniques offered by reputable organizations or institutions.
-
Attend the workshop and actively participate in the sessions.
-
Take notes and ask questions to clarify any concepts or techniques.
-
Follow up with the workshop organizers or speakers to further explore the topics covered.
Write a blog post or article on a specific deep learning application
Show steps
Encourages students to explore real-world applications of deep learning and develop their communication skills.
Browse courses on
Deep Learning Applications
Show steps
-
Choose a specific deep learning application, such as image recognition, natural language processing, or time series forecasting.
-
Research and gather information on the application, including its benefits, challenges, and potential use cases.
-
Write a blog post or article that explains the application, its technical details, and its potential impact.
-
Share your blog post or article with others and encourage feedback.
Build a deep learning model for a real-world problem
Show steps
Challenges students to apply their knowledge and skills to solve a real-world problem, promoting practical application and critical thinking.
Show steps
-
Identify a real-world problem that can be addressed using deep learning.
-
Gather and prepare a dataset relevant to the problem.
-
Design and implement a deep learning model to solve the problem.
-
Evaluate the performance of your model and make improvements as needed.
-
Present your findings and insights to others.
Review matrix computations
Show steps
Reinforces foundational linear algebra knowledge that is essential for understanding deep learning models.
Browse courses on
Matrix Computations
Show steps
Show steps
Show steps
- Go through your notes from a previous linear algebra course or textbook.
- Solve practice problems on matrix operations, such as addition, subtraction, multiplication, and inversion.
- Use an online matrix calculator to check your answers and gain a better understanding of matrix operations.
Read 'Deep Learning' by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Show steps
Provides a comprehensive overview of deep learning theory and techniques, complementing the course material and offering a deeper understanding of the field.
View
Deep Learning
on Amazon
Show steps
Show steps
Show steps
- Acquire a copy of the book 'Deep Learning' by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
- Set aside dedicated time for reading and studying the book.
- Take notes and highlight important concepts and techniques.
- Work through the exercises and examples provided in the book to reinforce your understanding.
Practice building and training simple neural networks in TensorFlow
Show steps
Provides hands-on experience in implementing and training neural networks, which is crucial for understanding the practical aspects of deep learning.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Follow tutorials or online courses on building neural networks in TensorFlow.
- Create a simple neural network model for a classification or regression task.
- Train and evaluate your model on a dataset.
- Experiment with different hyperparameters to optimize the performance of your model.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Form a study group with classmates to discuss course material and work on projects
Show steps
Fosters collaboration, enhances understanding through peer-to-peer learning, and provides support for completing projects.
Show steps
Show steps
Show steps
- Identify a few classmates who are interested in forming a study group.
- Set regular meeting times and locations that work for everyone in the group.
- Create a schedule for reviewing course material, discussing concepts, and working on projects together.
- Take turns leading discussions and presenting on specific topics.
Follow online tutorials on specific TensorFlow libraries or tools
Show steps
Provides hands-on experience with specific TensorFlow libraries and tools, enhancing students' practical knowledge and ability to use them effectively.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Identify a specific TensorFlow library or tool that you want to learn more about.
- Search for and select reputable online tutorials that cover the library or tool.
- Follow the tutorials step-by-step, practicing the techniques and concepts being taught.
- Experiment with the library or tool on your own to further explore its capabilities.
Attend a workshop on advanced TensorFlow techniques
Show steps
Provides exposure to cutting-edge TensorFlow techniques and best practices, which can significantly enhance students' understanding and skills.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Identify and register for a workshop on advanced TensorFlow techniques offered by reputable organizations or institutions.
- Attend the workshop and actively participate in the sessions.
- Take notes and ask questions to clarify any concepts or techniques.
- Follow up with the workshop organizers or speakers to further explore the topics covered.
Write a blog post or article on a specific deep learning application
Show steps
Encourages students to explore real-world applications of deep learning and develop their communication skills.
Browse courses on
Deep Learning Applications
Show steps
Show steps
Show steps
- Choose a specific deep learning application, such as image recognition, natural language processing, or time series forecasting.
- Research and gather information on the application, including its benefits, challenges, and potential use cases.
- Write a blog post or article that explains the application, its technical details, and its potential impact.
- Share your blog post or article with others and encourage feedback.
Build a deep learning model for a real-world problem
Show steps
Challenges students to apply their knowledge and skills to solve a real-world problem, promoting practical application and critical thinking.
Show steps
Show steps
Show steps
- Identify a real-world problem that can be addressed using deep learning.
- Gather and prepare a dataset relevant to the problem.
- Design and implement a deep learning model to solve the problem.
- Evaluate the performance of your model and make improvements as needed.
- Present your findings and insights to others.
Career center
Learners who complete Sequences, Time Series and Prediction will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
As a Data Scientist, you may be responsible for developing novel methodologies to analyze time series data, including forecasting and prediction. This course provides a strong foundation in the theoretical concepts and practical techniques used for time series analysis, particularly in the context of deep neural networks. By understanding the principles of sequence prediction and mastering the tools to implement them, you'll be well-equipped to excel in this role.
Machine Learning Engineer
Machine Learning Engineer
As a Machine Learning Engineer, you may be tasked with designing and implementing machine learning solutions for various applications, including time series forecasting and prediction. This course delves into advanced techniques like recurrent neural networks and convolutional neural networks, providing you with the knowledge and skills to tackle complex time series problems. It can significantly enhance your ability to build and deploy effective machine learning models for real-world scenarios.
Quantitative Analyst
Quantitative Analyst
As a Quantitative Analyst, you may specialize in analyzing financial time series data to identify patterns, trends, and relationships. This course provides a rigorous understanding of time series analysis and prediction techniques, enabling you to develop sophisticated models for financial forecasting and risk assessment. By mastering the concepts and tools covered in this course, you'll gain a competitive edge in the field of quantitative finance.
Data Analyst
Data Analyst
As a Data Analyst, you may be involved in processing, analyzing, and interpreting time series data to extract meaningful insights. This course provides a comprehensive overview of time series analysis techniques, including pre-processing, feature engineering, and model selection. By acquiring proficiency in these methods, you'll be well-equipped to derive valuable information from sequential data and contribute to data-driven decision-making.
Business Intelligence Analyst
Business Intelligence Analyst
As a Business Intelligence Analyst, you may be responsible for analyzing time series data to identify trends, patterns, and anomalies that can inform business decisions. This course offers a practical approach to time series analysis, covering techniques for data visualization, forecasting, and anomaly detection. By mastering these skills, you'll gain a deeper understanding of business operations and contribute to data-driven strategies.
Software Engineer
Software Engineer
As a Software Engineer specializing in machine learning, you may be involved in developing and deploying time series prediction models. This course provides a solid foundation in the theory and implementation of time series analysis using TensorFlow, a widely adopted open-source framework. By gaining proficiency in these techniques, you'll be well-equipped to build scalable and efficient machine learning solutions for real-world problems.
Statistician
Statistician
As a Statistician specializing in time series analysis, you may be responsible for developing statistical models to forecast and predict future trends. This course offers a comprehensive overview of time series analysis techniques, covering both classical and modern approaches. By mastering these methods, you'll gain a deep understanding of the statistical principles and methodologies used in time series analysis, enhancing your ability to solve complex problems in various fields.
Financial Analyst
Financial Analyst
As a Financial Analyst, you may be involved in analyzing financial time series data to make investment recommendations. This course provides a practical introduction to time series analysis techniques, covering methods for data pre-processing, feature extraction, and model evaluation. By acquiring proficiency in these skills, you'll be well-equipped to identify trends, patterns, and relationships in financial data, enabling you to make informed investment decisions.
Actuary
Actuary
As an Actuary, you may be responsible for assessing and managing financial risks using statistical and mathematical techniques. This course provides a solid foundation in time series analysis, covering methods for modeling and forecasting time-dependent data. By mastering these techniques, you'll gain a deeper understanding of the principles and practices used in actuarial science, enhancing your ability to develop and implement risk management strategies.
Epidemiologist
Epidemiologist
As an Epidemiologist, you may be involved in analyzing time series data to track and predict the spread of diseases. This course offers an introduction to time series analysis techniques, providing you with the skills to analyze and interpret epidemiological data. By gaining proficiency in these methods, you'll be well-equipped to contribute to public health efforts and develop effective strategies for disease prevention and control.
Operations Research Analyst
Operations Research Analyst
As an Operations Research Analyst, you may be responsible for analyzing and optimizing complex systems, including those involving time-dependent data. This course provides a practical overview of time series analysis techniques, covering methods for modeling, forecasting, and simulating time series data. By acquiring proficiency in these skills, you'll be well-equipped to develop and implement data-driven solutions to improve operational efficiency and decision-making.
Risk Manager
Risk Manager
As a Risk Manager, you may be involved in identifying, assessing, and mitigating risks across various domains. This course offers an introduction to time series analysis techniques, providing you with the skills to analyze and interpret time-dependent data. By gaining proficiency in these methods, you'll be well-equipped to develop and implement risk management strategies that account for temporal dependencies and uncertainties.
Data Engineer
Data Engineer
As a Data Engineer, you may be responsible for designing and building data pipelines that process and prepare time series data for analysis. This course provides a practical overview of time series analysis techniques, covering methods for data pre-processing, feature extraction, and data transformation. By acquiring proficiency in these skills, you'll be well-equipped to develop and maintain scalable data pipelines that support data-driven decision-making.
Product Manager
Product Manager
As a Product Manager, you may be involved in defining and developing data-driven products that leverage time series analysis. This course offers an introduction to time series analysis techniques, providing you with the foundational knowledge to understand how time-dependent data can be used to inform product decisions. By gaining proficiency in these methods, you'll be well-equipped to collaborate with engineers and data scientists to build products that meet the evolving needs of users.
Business Analyst
Business Analyst
As a Business Analyst, you may be responsible for analyzing and interpreting time series data to identify trends and patterns that can inform business decisions. This course provides a practical overview of time series analysis techniques, covering methods for data visualization, forecasting, and anomaly detection. By acquiring proficiency in these skills, you'll be well-equipped to extract valuable insights from time series data and contribute to data-driven decision-making.
For more career information including salaries, visit:
OpenCourser.com/course/1nqwix/sequences
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
Sequences, Time Series and Prediction.
Provides a comprehensive overview of artificial intelligence. The book covers a wide range of topics, including machine learning, natural language processing, and computer vision. The book valuable resource for anyone who wants to learn more about artificial intelligence.
Provides a comprehensive overview of statistical learning. The book covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning. The book valuable resource for anyone who wants to learn more about statistical learning.
Save
Provides a comprehensive overview of deep learning. The book covers a wide range of topics, including neural networks, convolutional neural networks, and recurrent neural networks. The book valuable resource for anyone who wants to learn more about deep learning.
Provides a comprehensive overview of machine learning with Python. The book covers a wide range of topics, including data preparation, feature engineering, and model selection. The book valuable resource for anyone who wants to learn more about machine learning.
Provides a comprehensive overview of deep learning with Python. The book covers a wide range of topics, including neural networks, convolutional neural networks, and recurrent neural networks. The book valuable resource for anyone who wants to learn more about deep learning.
Save
Provides a comprehensive overview of forecasting methods, with a focus on practical applications. It valuable resource for anyone who wants to learn more about forecasting.
Provides a practical guide to time series analysis. The book covers a wide range of topics, including data preparation, forecasting, and model evaluation. The book valuable resource for anyone who wants to learn more about time series analysis.
Save
This classic book comprehensive treatment of time series analysis and forecasting, with a focus on Box-Jenkins models. It valuable resource for anyone interested in learning more about the topic.
This paper provides a comprehensive overview of recurrent neural networks (RNNs) and long short-term memory (LSTM) networks. The paper discusses the architecture of RNNs and LSTMs, as well as their training and application. The paper valuable resource for anyone interested in learning more about RNNs and LSTMs.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/1nqwix/sequences
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Effort
4 weeks of study, 4-5 hours/week
Level
Intermediate
Via
Coursera
Institution
DeepLearning.AI
Instructor
Laurence Moroney
Language
English
Transcripts
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آذربایجان دیلی
This course belongs to a
collection
called
DeepLearning.AI TensorFlow Developer. It's comprised of four courses:
- Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
- Convolutional Neural Networks in TensorFlow
- Sequences, Time Series and Prediction (viewing)
- Natural Language Processing in TensorFlow
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