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Amit Yadav
In this 1 hour long project based course, you will learn to save, load and restore models with Keras. In Keras, we can save just the model weights, or we can save weights along with the entire model architecture. We can also export the models to TensorFlow's Saved Mode format which is very useful when serving a model in production, and we can load models from the Saved Model format back in Keras as well.
Read more
In this 1 hour long project based course, you will learn to save, load and restore models with Keras. In Keras, we can save just the model weights, or we can save weights along with the entire model architecture. We can also export the models to TensorFlow's Saved Mode format which is very useful when serving a model in production, and we can load models from the Saved Model format back in Keras as well.
Read more
In this 1 hour long project based course, you will learn to save, load and restore models with Keras. In Keras, we can save just the model weights, or we can save weights along with the entire model architecture. We can also export the models to TensorFlow's Saved Mode format which is very useful when serving a model in production, and we can load models from the Saved Model format back in Keras as well.
In order to be successful in this project, you should be familiar with python programming, and basics of neural networks.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/1vlzr7/save
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What's inside
Syllabus
Save, Load, and Export Models with Keras
Welcome to this project-based course on how to save, load, and export models with Keras. In this 1 hour long project, you will learn to save, load, and restore models with Keras. In Keras, we can save just the model weights, or we can save weights along with the entire model architecture. We can also export the models to TensorFlow's Saved Mode format which is very useful when serving a model in production, and we can load models from the Saved Model format back in Keras as well.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches concepts that are standard in industry
Suitable for students who are new to model saving and loading with Keras
Shows how to export models to the TensorFlow Saved Model format
Demonstrates how to save just the model weights
Presents a practical project-based approach
Involves saving weights along with the model architecture
Requires familiarity with Python programming and neural networks
May require additional resources for students with different backgrounds
Save this course
Save Save, Load and Export Models with Keras to your list so you can find it easily later:
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Reviews summary
Well-regarded keras saving course
Learners say this very useful course on saving, loading, and exporting Keras models has detailed explanations. Students report that the rhyme text is tiny and blurry, and that there may be technical issues in creating checkpoints. However, most students largely positive experiences with this Keras course.
Learners find this course to be helpful.
"Great course on saving and loading models."
"I really enjoyed working with this project."
"Detailed explanations of various Keras save options, and their parameters."
Some learners experienced technical issues.
"I had a technical issue when creating the checkpoints"
"Rhyme texts are so tiny and blurry to follow, the virtual environment takes too much time to load."
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 Save, Load and Export Models with Keras with these
activities:
Read 'Deep Learning with Python'
Show steps
Review foundational materials and principles in preparation for the course on saving, loading and restoring models with Keras.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
-
Obtain a copy of the book.
-
Read the first three chapters.
-
Complete the exercises in the first three chapters.
Organize course materials for future reference
Show steps
Improve retention of course materials by organizing them for easy access and review.
Show steps
-
Gather all notes, assignments, quizzes, and exams.
-
Create a system for organizing the materials.
-
Review the materials periodically.
Follow TensorFlow tutorial on saving and restoring models
Show steps
Apply best practices for saving and restoring models to reinforce skills learned in this course.
Browse courses on
Keras
Show steps
-
Navigate to the TensorFlow tutorial.
-
Follow the steps in the tutorial.
-
Run the code examples.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Participate in a study group
Show steps
Enhance understanding of course concepts through collaborative learning and discussion with peers.
Show steps
-
Find a study group or create your own.
-
Meet regularly to discuss course materials.
-
Work together on assignments and projects.
Practice saving and restoring different types of models
Show steps
Solidify understanding of saving and restoring models by practicing with various model types.
Browse courses on
Keras
Show steps
-
Create a sequential model.
-
Save the sequential model.
-
Restore the sequential model.
-
Create a functional model.
-
Save the functional model.
-
Restore the functional model.
Build a project that utilizes saving and restoring of models
Show steps
Demonstrate proficiency in saving and restoring models by applying these skills to a practical project.
Browse courses on
Keras
Show steps
-
Define the project scope and goals.
-
Gather and prepare the data.
-
Build the model.
-
Train the model.
-
Save the model.
-
Restore the model.
-
Evaluate the model.
Participate in a Kaggle competition related to Keras
Show steps
Apply skills learned in this course to a real-world problem and receive feedback from experts.
Browse courses on
Kaggle
Show steps
-
Identify a relevant Kaggle competition.
-
Download the competition data.
-
Build a model using Keras.
-
Submit your model to the competition.
-
Analyze the results and improve your model.
Contribute to a Keras open-source project
Show steps
Gain practical experience and contribute to the Keras community by participating in open-source projects.
Browse courses on
Open Source
Show steps
-
Identify a Keras open-source project.
-
Read the project documentation.
-
Identify an area where you can contribute.
-
Fork the project repository.
-
Make your changes.
-
Submit a pull request.
Read 'Deep Learning with Python'
Show steps
Review foundational materials and principles in preparation for the course on saving, loading and restoring models with Keras.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
Show steps
Show steps
- Obtain a copy of the book.
- Read the first three chapters.
- Complete the exercises in the first three chapters.
Organize course materials for future reference
Show steps
Improve retention of course materials by organizing them for easy access and review.
Show steps
Show steps
Show steps
- Gather all notes, assignments, quizzes, and exams.
- Create a system for organizing the materials.
- Review the materials periodically.
Follow TensorFlow tutorial on saving and restoring models
Show steps
Apply best practices for saving and restoring models to reinforce skills learned in this course.
Browse courses on
Keras
Show steps
Show steps
Show steps
- Navigate to the TensorFlow tutorial.
- Follow the steps in the tutorial.
- Run the code examples.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Participate in a study group
Show steps
Enhance understanding of course concepts through collaborative learning and discussion with peers.
Show steps
Show steps
Show steps
- Find a study group or create your own.
- Meet regularly to discuss course materials.
- Work together on assignments and projects.
Practice saving and restoring different types of models
Show steps
Solidify understanding of saving and restoring models by practicing with various model types.
Browse courses on
Keras
Show steps
Show steps
Show steps
- Create a sequential model.
- Save the sequential model.
- Restore the sequential model.
- Create a functional model.
- Save the functional model.
- Restore the functional model.
Build a project that utilizes saving and restoring of models
Show steps
Demonstrate proficiency in saving and restoring models by applying these skills to a practical project.
Browse courses on
Keras
Show steps
Show steps
Show steps
- Define the project scope and goals.
- Gather and prepare the data.
- Build the model.
- Train the model.
- Save the model.
- Restore the model.
- Evaluate the model.
Participate in a Kaggle competition related to Keras
Show steps
Apply skills learned in this course to a real-world problem and receive feedback from experts.
Browse courses on
Kaggle
Show steps
Show steps
Show steps
- Identify a relevant Kaggle competition.
- Download the competition data.
- Build a model using Keras.
- Submit your model to the competition.
- Analyze the results and improve your model.
Contribute to a Keras open-source project
Show steps
Gain practical experience and contribute to the Keras community by participating in open-source projects.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Identify a Keras open-source project.
- Read the project documentation.
- Identify an area where you can contribute.
- Fork the project repository.
- Make your changes.
- Submit a pull request.
Career center
Learners who complete Save, Load and Export Models with Keras will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Those who wish to become Machine Learning Engineers should take this course. It will teach foundational skills in various aspects of applying machine learning, which this role relies on. This course would help you to load and save machine learning models so they can be shared with others, used in different software environments, and their development can be continued later.
Data Scientist
Data Scientist
If you are seeking a career as a Data Scientist, this course would be very useful. It is crucial for Data Scientists to be able to load and save machine learning models in order to share their results, continue the development of their machine learning models later, and use their models in other software environments. This course would give you a foundation in these skills.
Software Engineer
Software Engineer
This course may be useful for those seeking to become Software Engineers. Software Engineers need to be able to use machine learning models, and this course will give you the ability to load and save these models and use them in your own software, which will make you a more effective Software Engineer.
Data Analyst
Data Analyst
This course may be useful for Data Analysts. To be successful, Data Analysts need to be able to work with machine learning models, and this course will give you the ability to load and save these models so that you can run analysis on them and use them in your data analysis work more effectively.
Quantitative Analyst
Quantitative Analyst
Those seeking to become Quantitative Analysts may find this course useful. This course will help you to work with machine learning models, giving you the ability to load and save these models for use in your quantitative analysis.
Product Manager
Product Manager
This course would be helpful for Product Managers who wish to implement machine learning models in their products. This course will teach you how to load and save these models for use in your products.
Business Analyst
Business Analyst
Those seeking to become Business Analysts may find this course useful. It will teach you how to load and save machine learning models, which can be helpful in analyzing data for your clients.
Consultant
Consultant
This course will be useful to Consultants who implement machine learning solutions for their clients. It will teach you how to save and load machine learning models so that you can share your models with your clients and they can continue the development of their own machine learning solutions.
Financial Analyst
Financial Analyst
This course may be useful for Financial Analysts who need to be able to create and use machine learning models to evaluate investments. It will teach you how to load and save machine learning models so that you can share them with your team and continue the development of your models in the future.
Marketing Analyst
Marketing Analyst
This course may be useful for those who want to be Marketing Analysts. It will teach you how to load and save machine learning models that can be used to improve your marketing campaigns.
Actuary
Actuary
This course may be useful for Actuaries who need to use machine learning models to assess risk. It will teach you how to load and save these models so that you can easily share them with your colleagues and continue their development later on.
For more career information including salaries, visit:
OpenCourser.com/course/1vlzr7/save
Reading list
We've selected eight 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
Save, Load and Export Models with Keras.
The official documentation for Keras, providing detailed information on the library's features and functions. Essential as a reference.
Save
A comprehensive textbook on deep learning, covering advanced topics. Valuable as a reference or for further exploration after completing the course.
Provides a solid foundation in deep learning concepts and Keras, the library used in this course. Suitable as a companion textbook or for additional reading.
Offers a practical guide to machine learning with a focus on Keras and TensorFlow, providing hands-on examples and exercises.
Offers a practical approach to machine learning, focusing on real-world applications. Can provide additional perspectives on model deployment and usage.
Offers a theoretical foundation in machine learning, including probabilistic models. Valuable for understanding the underlying principles of model saving and loading.
Provides an alternative perspective on deep learning using the R programming language. Can offer additional insights into the concepts covered in the course.
Covers advanced topics in deep learning for natural language processing, expanding on the course's focus on saving and loading models.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/1vlzr7/save
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Effort
1 hour
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Amit Yadav
Language
English
Good to know
Teaches concepts that are standard in industry
Suitable for students who are new to model saving and loading with Keras
Shows how to export models to the TensorFlow Saved Model format
Demonstrates how to save just the model weights
Presents a practical project-based approach
Involves saving weights along with the model architecture
Requires familiarity with Python programming and neural networks
May require additional resources for students with different backgrounds
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