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Creating Custom Callbacks in Keras
Amit Yadav
Amit Yadav
In this 1.5-hour long project-based course, you will learn to create a custom callback function in Keras and use the callback during a model training process. We will implement the callback function to perform three tasks: Write a log file during the training process, plot the training metrics in a graph during the training process, and reduce the learning rate during the training with each epoch.
Read more
In this 1.5-hour long project-based course, you will learn to create a custom callback function in Keras and use the callback during a model training process. We will implement the callback function to perform three tasks: Write a log file during the training process, plot the training metrics in a graph during the training process, and reduce the learning rate during the training with each epoch.
Read more
In this 1.5-hour long project-based course, you will learn to create a custom callback function in Keras and use the callback during a model training process. We will implement the callback function to perform three tasks: Write a log file during the training process, plot the training metrics in a graph during the training process, and reduce the learning rate during the training with each epoch.
This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your Internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with (e.g. Python, Jupyter, and Tensorflow) pre-installed.
Prerequisites:
In order to be successful in this project, you should be familiar with Python, Neural Networks, and the Keras framework.
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/9hfet7/creating
What's inside
Syllabus
Creating Custom Callbacks in Keras
In this project, you will learn to create a custom callback function in Keras and use the callback during a model training process. We will implement the callback function to perform three tasks: Write a log file during the training process, plot the training metrics in a graph during the training process, and reduce the learning rate during the training with each epoch.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Taught by Amit Yadav, who is recognized for their work in deep learning and neural networks
Develops skills in creating custom callbacks in Keras, which is a popular machine learning library
Strengthens an existing foundation for intermediate learners in deep learning
Highly relevant to industry, as custom callbacks are used in real-world machine learning applications
Offers hands-on labs and interactive materials, providing practical experience
Requires familiarity with Python, Neural Networks, and the Keras framework, indicating that it targets learners with some prior knowledge
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Reviews summary
Keras custom callbacks
Learners say Creating Custom Callbacks in Keras is a short and targeted course that teaches practical skills which can be used to improve a developer's machine learning model development workflow. Those interested in this intermediate level course should already have a solid foundation in Keras and ML modeling. Reviewers have praised this course for its easy-to-follow structure and clear explanations.
Well-structured course with clear explanations
"Good explanation based upon in class to show how we can use keras"
Teaches valuable skills applicable to real-world ML projects
"You learn how to update learning rate and plot validation and train accuracy dynamically in jupyter notebook."
"This is a very targeted course so if you don't know how to train ML models on keras or don't know ML at all this course is not for you."
"The code needs to be slightly modified for newer version of keras and tensorflow but works perfectly in the Rhyme VM."
Requires strong foundation in Keras and ML
"This is a very targeted course so if you don't know how to train ML models on keras or don't know ML at all this course is not for you."
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 Creating Custom Callbacks in Keras with these
activities:
Read 'Deep Learning with Python' by François Chollet
Show steps
Gain a deeper understanding of Keras and its applications by reading a comprehensive book on deep learning, providing a solid foundation for the course.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
-
Obtain a copy of 'Deep Learning with Python'
-
Allocate time for reading and studying the relevant chapters
-
Take notes and engage with the material to enhance comprehension
Review Keras fundamentals
Show steps
Review the foundational concepts of Keras, ensuring a strong understanding of the framework before starting the course.
Browse courses on
Neural Networks
Show steps
-
Revisit Keras documentation and tutorials
-
Complete online exercises or practice coding problems using Keras
Show all two activities
Read 'Deep Learning with Python' by François Chollet
Show steps
Gain a deeper understanding of Keras and its applications by reading a comprehensive book on deep learning, providing a solid foundation for the course.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
Show steps
Show steps
- Obtain a copy of 'Deep Learning with Python'
- Allocate time for reading and studying the relevant chapters
- Take notes and engage with the material to enhance comprehension
Review Keras fundamentals
Show steps
Review the foundational concepts of Keras, ensuring a strong understanding of the framework before starting the course.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Revisit Keras documentation and tutorials
- Complete online exercises or practice coding problems using Keras
Show all two activities
Career center
Learners who complete Creating Custom Callbacks in Keras will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist uses their knowledge of programming, statistics, and machine learning to help solve business problems. Custom callbacks in Keras are a valuable tool for a Data Scientist because they allow for the monitoring and control of the training process. This course can help Data Scientists learn how to create custom callbacks to improve the performance of their models.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs, develops, and deploys machine learning models. Custom callbacks in Keras are a powerful tool for Machine Learning Engineers because they allow for the customization of the training process. This course can help Machine Learning Engineers learn how to create custom callbacks to improve the performance of their models.
Deep Learning Engineer
Deep Learning Engineer
A Deep Learning Engineer designs and develops deep learning models. Custom callbacks in Keras are a valuable tool for Deep Learning Engineers because they allow for the monitoring and control of the training process. This course can help Deep Learning Engineers learn how to create custom callbacks to improve the performance of their models.
Data Analyst
Data Analyst
A Data Analyst uses their knowledge of data and statistics to help businesses make better decisions. Custom callbacks in Keras can be a useful tool for Data Analysts because they allow for the monitoring and analysis of the training process. This course can help Data Analysts learn how to create custom callbacks to improve the performance of their models.
Project Manager
Project Manager
A Project Manager plans, executes, and controls projects. Custom callbacks in Keras can be a useful tool for Project Managers because they allow for the monitoring and analysis of the training process. This course can help Project Managers learn how to create custom callbacks to improve the performance of their models.
Data Engineer
Data Engineer
A Data Engineer designs, develops, and maintains data pipelines. Custom callbacks in Keras can be a useful tool for Data Engineers because they allow for the monitoring and analysis of the training process. This course can help Data Engineers learn how to create custom callbacks to improve the performance of their models.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses their knowledge of mathematics and statistics to analyze and predict financial data. Custom callbacks in Keras can be a useful tool for Quantitative Analysts because they allow for the monitoring and analysis of the training process. This course can help Quantitative Analysts learn how to create custom callbacks to improve the performance of their models.
Editor
Editor
An Editor reviews and edits written content for a variety of purposes. Custom callbacks in Keras can be a useful tool for Editors because they allow for the monitoring and analysis of the training process. This course can help Editors learn how to create custom callbacks to improve the performance of their models.
Teacher
Teacher
A Teacher educates students in a variety of subjects. Custom callbacks in Keras can be a useful tool for Teachers because they allow for the monitoring and analysis of the training process. This course can help Teachers learn how to create custom callbacks to improve the performance of their models.
Research Scientist
Research Scientist
A Research Scientist conducts research in a variety of fields, including science, engineering, and medicine. Custom callbacks in Keras can be a useful tool for Research Scientists because they allow for the monitoring and analysis of the training process. This course can help Research Scientists learn how to create custom callbacks to improve the performance of their models.
Writer
Writer
A Writer creates written content for a variety of purposes. Custom callbacks in Keras can be a useful tool for Writers because they allow for the monitoring and analysis of the training process. This course can help Writers learn how to create custom callbacks to improve the performance of their models.
Consultant
Consultant
A Consultant provides advice and guidance to businesses and organizations. Custom callbacks in Keras can be a useful tool for Consultants because they allow for the monitoring and analysis of the training process. This course can help Consultants learn how to create custom callbacks to improve the performance of their models.
Business Analyst
Business Analyst
A Business Analyst uses their knowledge of business and data to help businesses improve their operations. Custom callbacks in Keras can be a useful tool for Business Analysts because they allow for the monitoring and analysis of the training process. This course can help Business Analysts learn how to create custom callbacks to improve the performance of their models.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software applications. Custom callbacks in Keras can be a useful tool for Software Engineers because they allow for the customization and control of the training process. This course can help Software Engineers learn how to create custom callbacks to improve the performance of their models.
Product Manager
Product Manager
A Product Manager designs, develops, and launches new products. Custom callbacks in Keras can be a useful tool for Product Managers because they allow for the monitoring and analysis of the training process. This course can help Product Managers learn how to create custom callbacks to improve the performance of their models.
For more career information including salaries, visit:
OpenCourser.com/course/9hfet7/creating
Reading list
We've selected ten 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
Creating Custom Callbacks in Keras.
Provides a comprehensive introduction to deep learning, covering the fundamentals of neural networks, convolutional neural networks, recurrent neural networks, and more. It valuable resource for anyone looking to learn more about deep learning and its applications.
Provides a practical introduction to machine learning, covering the basics of supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for anyone looking to learn more about machine learning and its applications.
Provides a comprehensive introduction to machine learning using the Python programming language. It covers the fundamentals of supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for anyone looking to learn more about machine learning and its applications using Python.
Provides a comprehensive introduction to machine learning using the C++ programming language. It covers the fundamentals of supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for anyone looking to learn more about machine learning and its applications using C++.
Provides a comprehensive overview of deep learning techniques for natural language processing (NLP). It covers various NLP tasks such as text classification, sentiment analysis, machine translation, and question answering, and provides practical examples and exercises to help readers implement and apply deep learning models for NLP tasks.
Provides a comprehensive overview of machine learning techniques using Python. It covers various machine learning algorithms, including supervised learning, unsupervised learning, and deep learning, and provides practical examples and exercises to help readers implement and apply machine learning models.
Provides a comprehensive overview of deep learning techniques using Python. It covers various deep learning architectures, such as convolutional neural networks, recurrent neural networks, and transformers, and provides practical examples and exercises to help readers implement and apply deep learning models.
Provides a comprehensive overview of machine learning techniques using R, a popular statistical programming language. It covers various machine learning algorithms, including supervised learning, unsupervised learning, and deep learning, and provides practical examples and exercises to help readers implement and apply machine learning models.
Provides a visual and intuitive introduction to deep learning concepts and algorithms. It uses clear and concise language, along with numerous illustrations and diagrams, to help readers understand the inner workings of deep learning models.
Save
Provides a comprehensive introduction to deep learning using the R programming language. It covers the fundamentals of neural networks, convolutional neural networks, recurrent neural networks, and more. It valuable resource for anyone looking to learn more about deep learning and its applications using R.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/9hfet7/creating
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Effort
1.5 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Amit Yadav
Language
English
Good to know
Taught by Amit Yadav, who is recognized for their work in deep learning and neural networks
Develops skills in creating custom callbacks in Keras, which is a popular machine learning library
Strengthens an existing foundation for intermediate learners in deep learning
Highly relevant to industry, as custom callbacks are used in real-world machine learning applications
Offers hands-on labs and interactive materials, providing practical experience
Requires familiarity with Python, Neural Networks, and the Keras framework, indicating that it targets learners with some prior knowledge
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