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Hyperparameter Tuning with Keras Tuner
Amit Yadav
Amit Yadav
In this 2-hour long guided project, we will use Keras Tuner to find optimal hyperparamters for a Keras model. Keras Tuner is an open source package for Keras which can help machine learning practitioners automate Hyperparameter tuning tasks for their Keras models. The concepts learned in this project will apply across a variety of model architectures and problem scenarios. Please note that we are going to learn to use Keras Tuner for hyperparameter tuning, and are not going to implement the tuning algorithms ourselves. At the time of recording this project, Keras Tuner has a few tuning algorithms including Random Search, Bayesian Optimization and HyperBand.
Read more
In this 2-hour long guided project, we will use Keras Tuner to find optimal hyperparamters for a Keras model. Keras Tuner is an open source package for Keras which can help machine learning practitioners automate Hyperparameter tuning tasks for their Keras models. The concepts learned in this project will apply across a variety of model architectures and problem scenarios. Please note that we are going to learn to use Keras Tuner for hyperparameter tuning, and are not going to implement the tuning algorithms ourselves. At the time of recording this project, Keras Tuner has a few tuning algorithms including Random Search, Bayesian Optimization and HyperBand.
Read more
In this 2-hour long guided project, we will use Keras Tuner to find optimal hyperparamters for a Keras model. Keras Tuner is an open source package for Keras which can help machine learning practitioners automate Hyperparameter tuning tasks for their Keras models. The concepts learned in this project will apply across a variety of model architectures and problem scenarios. Please note that we are going to learn to use Keras Tuner for hyperparameter tuning, and are not going to implement the tuning algorithms ourselves. At the time of recording this project, Keras Tuner has a few tuning algorithms including Random Search, Bayesian Optimization and HyperBand.
In order to complete this project successfully, you will need prior programming experience in Python. This is a practical, hands on guided project for learners who already have theoretical understanding of Neural Networks, and optimization algorithms like gradient descent but want to understand how to use Keras Tuner to start optimizing hyperparameters for training their Keras models. You should also be familiar with the Keras API.
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/uulkzt/hyperparameter
What's inside
Syllabus
Hyperparameter Tuning with Keras Tuner
In this 2-hour long guided project, we will use Keras Tuner to find optimal hyperparamters for a Keras model. Keras Tuner is an open source package for Keras which can help machine learning practitioners automate Hyperparameter tuning tasks for their Keras models. The concepts learned in this project will apply across a variety of model architectures and problem scenarios. Please note that we are going to learn to use Keras Tuner for hyperparameter tuning, and are not going to implement the tuning algorithms ourselves. At the time of recording this project, Keras Tuner has a few tuning algorithms including Random Search, Bayesian Optimization and HyperBand.
In order to complete this project successfully, you will need prior programming experience in Python. This is a practical, hands on guided project for learners who already have theoretical understanding of Neural Networks, and optimization algorithms like gradient descent but want to understand how to use Keras Tuner to start optimizing hyperparameters for training their Keras models. You should also be familiar with the Keras API.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops proficiency in automating hyperparameter tuning for Keras models, a valuable skill for machine learning practitioners
Provides hands-on practice with Keras Tuner, making it easier to apply theoretical knowledge in practical settings
Taught by Amit Yadav, who has expertise in Keras and hyperparameter tuning
Suitable for learners with a foundation in Neural Networks and optimization algorithms
Focuses on optimizing hyperparameters, a key aspect of improving model performance
Requires prior programming experience in Python and familiarity with the Keras API
May be less accessible to learners who are new to machine learning or deep learning
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Reviews summary
Excellent course on hyperparameter tuning
Students say this excellent course provides a helpful foundation for using Keras Tuner for deep learning. It features engaging lectures that are well taught, though some students suggest it could be improved with more in-depth explanations. Note that the course may need to be updated to the latest version of Keras Tuner, as the version used in this project is now deprecated.
The instructor explains concepts clearly.
"The manner it was explained was amazing"
"The course is well taught."
Students largely enjoyed this course.
"Excellent"
"Helpful foundation course"
"Very beneficial"
"I loved it"
Some students suggest more in-depth explanations would be helpful.
"A bit more insight into concepts would have been better."
The course may need to be updated to the latest version of Keras Tuner.
"The kerastuner used in this project is now deprecated."
Activities
Coming soon
We're preparing activities for
Hyperparameter Tuning with Keras Tuner.
These are activities you can do either before, during, or after a course.
Career center
Learners who complete Hyperparameter Tuning with Keras Tuner will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use their knowledge of data analysis, machine learning, and statistics to extract insights from data. This course in Hyperparameter Tuning with Keras Tuner can be beneficial for Data Scientists, as it provides practical experience in optimizing hyperparameters for training Keras models, a valuable skill for building accurate and efficient machine learning models.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers leverage their expertise in developing and deploying machine learning models to solve complex business problems. This course in Hyperparameter Tuning with Keras Tuner can be a valuable asset for aspiring Machine Learning Engineers, as it provides hands-on experience in optimizing hyperparameters for training Keras models, a key skill for building high-performing machine learning solutions.
Deep Learning Engineer
Deep Learning Engineer
Deep Learning Engineers specialize in developing and deploying deep learning models for various applications. This course in Hyperparameter Tuning with Keras Tuner can be highly beneficial for Deep Learning Engineers, as it provides practical experience in optimizing hyperparameters for training Keras models, a crucial skill for building state-of-the-art deep learning models.
Machine Learning Researcher
Machine Learning Researcher
Machine Learning Researchers conduct research in the field of machine learning and develop new algorithms and techniques. This course in Hyperparameter Tuning with Keras Tuner can be beneficial for Machine Learning Researchers who are working on projects involving Keras models, as it provides practical experience in optimizing hyperparameters for training Keras models, a valuable skill for building innovative and groundbreaking machine learning solutions.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design, develop, and deploy artificial intelligence systems. This course in Hyperparameter Tuning with Keras Tuner can be beneficial for Artificial Intelligence Engineers who are working on projects involving Keras models, as it provides practical experience in optimizing hyperparameters for training Keras models, a valuable skill for building intelligent and autonomous systems.
Computer Scientist
Computer Scientist
Computer Scientists conduct research in the field of computer science and develop new algorithms and techniques. This course in Hyperparameter Tuning with Keras Tuner may be useful for Computer Scientists who are interested in specializing in machine learning, as it provides hands-on experience in optimizing hyperparameters for training Keras models, a valuable skill for building innovative and groundbreaking machine learning solutions.
Research Scientist
Research Scientist
Research Scientists conduct research in various scientific fields, including machine learning and artificial intelligence. This course in Hyperparameter Tuning with Keras Tuner can be beneficial for Research Scientists who are working on projects involving Keras models, as it provides practical experience in optimizing hyperparameters for training Keras models, a valuable skill for building innovative and groundbreaking machine learning solutions.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. While this course in Hyperparameter Tuning with Keras Tuner may not directly prepare one for all aspects of software engineering, it can be beneficial for Software Engineers who are interested in specializing in machine learning or deep learning, as it provides hands-on experience in optimizing hyperparameters for training Keras models, a valuable skill for building robust and scalable machine learning applications.
Software Developer
Software Developer
Software Developers design, develop, and maintain software applications. This course in Hyperparameter Tuning with Keras Tuner may be useful for Software Developers who are interested in specializing in machine learning or deep learning, as it provides hands-on experience in optimizing hyperparameters for training Keras models, a valuable skill for building robust and scalable machine learning applications.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. This course in Hyperparameter Tuning with Keras Tuner may be useful for Quantitative Analysts who are interested in incorporating machine learning techniques into their work, as it provides hands-on experience in optimizing hyperparameters for training Keras models, a valuable skill for building predictive models for financial analysis.
Data Analyst
Data Analyst
Data Analysts collect, process, and analyze data to extract insights and inform decision-making. This course in Hyperparameter Tuning with Keras Tuner may be useful for Data Analysts who are interested in specializing in machine learning, as it provides hands-on experience in optimizing hyperparameters for training Keras models, a valuable skill for building predictive models for data analysis.
Statistician
Statistician
Statisticians use statistical methods to collect, analyze, interpret, and present data. This course in Hyperparameter Tuning with Keras Tuner may be useful for Statisticians who are interested in specializing in machine learning, as it provides hands-on experience in optimizing hyperparameters for training Keras models, a valuable skill for building statistical models for data analysis and prediction.
Product Manager
Product Manager
Product Managers are responsible for managing the development and launch of new products. This course in Hyperparameter Tuning with Keras Tuner may be useful for Product Managers who are working on machine learning-based products, as it provides hands-on experience in optimizing hyperparameters for training Keras models, a valuable skill for building successful and user-friendly machine learning products.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines and infrastructure. This course in Hyperparameter Tuning with Keras Tuner may be useful for Data Engineers who are interested in specializing in machine learning, as it provides hands-on experience in optimizing hyperparameters for training Keras models, a valuable skill for building efficient and scalable machine learning pipelines.
Business Analyst
Business Analyst
Business Analysts use data analysis and problem-solving skills to improve business processes and make data-driven decisions. This course in Hyperparameter Tuning with Keras Tuner may be useful for Business Analysts who are interested in incorporating machine learning techniques into their work, as it provides hands-on experience in optimizing hyperparameters for training Keras models, a valuable skill for building predictive models for business analysis.
For more career information including salaries, visit:
OpenCourser.com/course/uulkzt/hyperparameter
Reading list
We've selected 13 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
Hyperparameter Tuning with Keras Tuner.
Save
Comprehensive reference on deep learning, covering a wide range of topics such as neural networks, convolutional neural networks, and recurrent neural networks. It good resource for researchers and practitioners who want to learn more about deep learning.
Comprehensive reference on pattern recognition and machine learning, covering a wide range of topics such as supervised learning, unsupervised learning, and reinforcement learning. It good resource for researchers and practitioners who want to learn more about pattern recognition and machine learning.
Save
Comprehensive introduction to deep learning, covering a wide range of topics such as neural networks, convolutional neural networks, and recurrent neural networks. It good resource for understanding the theoretical foundations of deep learning.
Save
Comprehensive introduction to computer vision, covering a wide range of topics such as image processing, object recognition, and video analysis.
Save
Comprehensive introduction to machine learning, covering a wide range of topics such as supervised learning, unsupervised learning, and reinforcement learning. It good resource for understanding the theoretical foundations of machine learning.
Comprehensive introduction to natural language processing with deep learning, covering a wide range of topics such as word embeddings, recurrent neural networks, and transformers.
Comprehensive introduction to speech and language processing, covering a wide range of topics such as speech recognition, natural language understanding, and machine translation.
Provides a comprehensive introduction to deep learning using the Python programming language. It covers the basics of deep learning such as neural networks, convolutional neural networks, and recurrent neural networks as well as more advanced topics such as hyperparameter tuning and model evaluation.
Comprehensive introduction to statistical learning with sparsity, covering a wide range of topics such as lasso regression, elastic net regression, and group lasso regression. It good resource for understanding the theoretical foundations of statistical learning with sparsity.
Comprehensive introduction to deep learning for natural language processing, covering a wide range of topics such as word embeddings, recurrent neural networks, and transformers.
Comprehensive introduction to convex optimization, covering a wide range of topics such as linear programming, quadratic programming, and semidefinite programming. It good resource for understanding the theoretical foundations of convex optimization.
Practical guide to machine learning using the Scikit-Learn, Keras, and TensorFlow libraries. It covers a wide range of topics such as data preprocessing, feature engineering, model training, and model evaluation.
Comprehensive introduction to numerical optimization, covering a wide range of topics such as gradient descent, Newton's method, and conjugate gradient methods.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/uulkzt/hyperparameter
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Amit Yadav
Language
English
Good to know
Develops proficiency in automating hyperparameter tuning for Keras models, a valuable skill for machine learning practitioners
Provides hands-on practice with Keras Tuner, making it easier to apply theoretical knowledge in practical settings
Taught by Amit Yadav, who has expertise in Keras and hyperparameter tuning
Suitable for learners with a foundation in Neural Networks and optimization algorithms
Focuses on optimizing hyperparameters, a key aspect of improving model performance
Requires prior programming experience in Python and familiarity with the Keras API
May be less accessible to learners who are new to machine learning or deep learning
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