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Amit Yadav
In this 2-hour long guided project, we will learn the basics of using Microsoft's Neural Network Intelligence (NNI) toolkit and will use it to run a Hyperparameter tuning experiment on a Neural Network. NNI is an open source, AutoML toolkit created by Microsoft which can help machine learning practitioners automate Feature engineering, Hyperparameter tuning, Neural Architecture search and Model compression. In this guided project, we are going to take a look at using NNI to perform hyperparameter tuning. Please note that we are going to learn to use the NNI toolkit for hyperparameter tuning, and are not going to implement the tuning algorithms ourselves. We will use the popular MNIST dataset and train a simple Neural Network to learn to classify images of hand-written digits from the dataset. Once a basic script is in place, we will use the NNI toolkit to run a hyperparameter tuning experiment to find optimal values for batch size, learning rate, choice of activation function for the hidden layer, number of hidden units for the hidden layer, and dropout rate for the dropout layer.
Read more
In this 2-hour long guided project, we will learn the basics of using Microsoft's Neural Network Intelligence (NNI) toolkit and will use it to run a Hyperparameter tuning experiment on a Neural Network. NNI is an open source, AutoML toolkit created by Microsoft which can help machine learning practitioners automate Feature engineering, Hyperparameter tuning, Neural Architecture search and Model compression. In this guided project, we are going to take a look at using NNI to perform hyperparameter tuning. Please note that we are going to learn to use the NNI toolkit for hyperparameter tuning, and are not going to implement the tuning algorithms ourselves. We will use the popular MNIST dataset and train a simple Neural Network to learn to classify images of hand-written digits from the dataset. Once a basic script is in place, we will use the NNI toolkit to run a hyperparameter tuning experiment to find optimal values for batch size, learning rate, choice of activation function for the hidden layer, number of hidden units for the hidden layer, and dropout rate for the dropout layer.
Read more
In this 2-hour long guided project, we will learn the basics of using Microsoft's Neural Network Intelligence (NNI) toolkit and will use it to run a Hyperparameter tuning experiment on a Neural Network. NNI is an open source, AutoML toolkit created by Microsoft which can help machine learning practitioners automate Feature engineering, Hyperparameter tuning, Neural Architecture search and Model compression. In this guided project, we are going to take a look at using NNI to perform hyperparameter tuning. Please note that we are going to learn to use the NNI toolkit for hyperparameter tuning, and are not going to implement the tuning algorithms ourselves. We will use the popular MNIST dataset and train a simple Neural Network to learn to classify images of hand-written digits from the dataset. Once a basic script is in place, we will use the NNI toolkit to run a hyperparameter tuning experiment to find optimal values for batch size, learning rate, choice of activation function for the hidden layer, number of hidden units for the hidden layer, and dropout rate for the dropout layer.
To be able to complete this project successfully, you should be familiar with the Python programming language. You should also be familiar with Neural Networks, TensorFlow and Keras.
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/skinbn/hyperparameter
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Syllabus
Hyperparameter Tuning with Microsoft NNI
In this 2-hour long guided project, we will learn the basics of using Microsoft's Neural Network Intelligence (NNI) toolkit and will use it to run a Hyperparameter tuning experiment on a Neural Network. NNI is an open source, AutoML toolkit created by Microsoft which can help machine learning practitioners automate Feature engineering, Hyperparameter tuning, Neural Architecture search and Model compression. In this guided project, we are going to take a look at using NNI to perform hyperparameter tuning. Please note that we are going to learn to use the NNI toolkit for hyperparameter tuning, and are not going to implement the tuning algorithms ourselves. We will use the popular MNIST dataset and train a simple Neural Network to learn to classify images of hand-written digits from the dataset. Once a basic script is in place, we will use the NNI toolkit to run a hyperparameter tuning experiment to find optimal values for batch size, learning rate, choice of activation function for the hidden layer, number of hidden units for the hidden layer, and dropout rate for the dropout layer.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Emphasizes the use of Microsoft's Neural Network Intelligence (NNI) toolkit for hyperparameter tuning, which aligns with industry best practices
Provides hands-on experience in hyperparameter tuning using a real-world dataset, fostering practical skills for learners
Introduces the open source NNI toolkit, enhancing learners' knowledge of available tools in the field
Assumes familiarity with Python, TensorFlow, and Keras, indicating it may be suitable for learners with prior knowledge in deep learning
Focuses on hyperparameter tuning for a neural network, which may not cover other aspects of machine learning model development
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Reviews summary
Rewarding neural network experience
According to students, Neural Network Intelligence is a rewarding learning experience with many great qualities. Students appreciate the engaging assignments and knowledgeable instructor who has a great teaching style. The course projects help learners gain a stronger comprehension of machine learning concepts.
Helps learners gain a stronger comprehension of machine learning concepts.
"helped me gain a stronger comprehension of machine learning concepts I'd learned from other courses"
Engaging course assignments and projects
"Good Short Project"
"creating an intuitive local webpage desgined to view the results of my models."
Knowledgeable instructor with great teaching style
"Great Instructor."
"I am looking forward to other projects that explore NNI capabilities"
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 Hyperparameter Tuning with Neural Network Intelligence with these
activities:
NNI Guided Tutorials
Show steps
Following a comprehensive tutorial on NNI will introduce you to the fundamental concepts of this essential toolkit and prepare you for success in the course.
Browse courses on
Hyperparameter Tuning
Show steps
-
Access the NNI documentation
-
Follow the step-by-step instructions
-
Complete the tutorial exercises
Show all one activities
NNI Guided Tutorials
Show steps
Following a comprehensive tutorial on NNI will introduce you to the fundamental concepts of this essential toolkit and prepare you for success in the course.
Browse courses on
Hyperparameter Tuning
Show steps
Show steps
Show steps
- Access the NNI documentation
- Follow the step-by-step instructions
- Complete the tutorial exercises
Show all one activities
Career center
Learners who complete Hyperparameter Tuning with Neural Network Intelligence will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists analyze, interpret, and visualize data to help organizations make informed decisions. They use a variety of tools and techniques, including machine learning, to extract insights from data. This course can help Data Scientists optimize their use of machine learning models by providing hands-on experience with the NNI toolkit. In particular, the course's focus on hyperparameter tuning will be highly relevant for Data Scientists who wish to improve the performance of their models.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build, deploy and maintain machine learning models that help organizations automate tasks, gain insights from data, and make better decisions. This course can provide someone interested in the field of Machine Learning Engineering with practical experience using Microsoft's Neural Network Intelligence (NNI) toolkit. Machine Learning Engineers who wish to become familiar with the NNI toolkit will find the hyperparameter tuning use case and Python programming instruction in this course particularly helpful.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to help organizations understand their customers, operations, and industries. They use a variety of tools and techniques, including machine learning, to identify trends and patterns in data. This course can help Data Analysts improve their understanding of machine learning and how to use it to analyze data more effectively. In particular, the course's focus on hyperparameter tuning will be helpful for Data Analysts who wish to improve the accuracy of their machine learning models.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They use a variety of programming languages and tools to create software that meets the needs of users. This course can help Software Engineers learn more about machine learning and how to use it to develop software applications. In particular, the course's focus on hyperparameter tuning will be helpful for Software Engineers who wish to improve the performance of their machine learning models.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design, develop, and maintain artificial intelligence systems. They use a variety of programming languages and tools to create AI systems that can perform tasks that would be difficult or impossible for humans to do. This course can help Artificial Intelligence Engineers learn more about machine learning and how to use it to develop AI systems. In particular, the course's focus on hyperparameter tuning will be helpful for Artificial Intelligence Engineers who wish to improve the performance of their AI systems.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. They use a variety of tools and techniques, including machine learning, to develop trading strategies and risk models. This course can help Quantitative Analysts improve their understanding of machine learning and how to use it to analyze financial data more effectively. In particular, the course's focus on hyperparameter tuning will be helpful for Quantitative Analysts who wish to improve the accuracy of their machine learning models.
Technical Product Manager
Technical Product Manager
Technical Product Managers work with engineers and designers to develop and launch new products. They use their understanding of technology and business to create products that meet the needs of users. This course can help Technical Product Managers learn more about machine learning and how to use it to develop new products. In particular, the course's focus on hyperparameter tuning will be helpful for Technical Product Managers who wish to improve the performance of their machine learning models.
Product Manager
Product Manager
Product Managers work with engineers, designers, and marketers to develop and launch new products. They use their understanding of customers and markets to create products that people want to use. This course can help Product Managers learn more about machine learning and how to use it to develop new products. In particular, the course's focus on hyperparameter tuning will be helpful for Product Managers who wish to improve the performance of their machine learning models.
Marketing Manager
Marketing Manager
Marketing Managers develop and execute marketing campaigns to promote products and services. They use a variety of marketing channels, including online and offline advertising, to reach target audiences. This course can help Marketing Managers learn more about machine learning and how to use it to improve their marketing campaigns. In particular, the course's focus on hyperparameter tuning will be helpful for Marketing Managers who wish to improve the performance of their machine learning models.
Sales Manager
Sales Manager
Sales Managers lead and motivate sales teams to achieve sales goals. They use a variety of sales strategies and techniques to close deals and generate revenue. This course can help Sales Managers learn more about machine learning and how to use it to improve their sales process. In particular, the course's focus on hyperparameter tuning will be helpful for Sales Managers who wish to improve the performance of their machine learning models.
Business Analyst
Business Analyst
Business Analysts analyze business processes and systems to identify areas for improvement. They use a variety of tools and techniques, including machine learning, to develop solutions that improve efficiency and effectiveness. This course can help Business Analysts learn more about machine learning and how to use it to improve their business analysis process. In particular, the course's focus on hyperparameter tuning will be helpful for Business Analysts who wish to improve the performance of their machine learning models.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to analyze and improve business processes. They use a variety of tools and techniques, including machine learning, to develop solutions that optimize efficiency and effectiveness. This course can help Operations Research Analysts learn more about machine learning and how to use it to improve their operations research process. In particular, the course's focus on hyperparameter tuning will be helpful for Operations Research Analysts who wish to improve the performance of their machine learning models.
Risk Analyst
Risk Analyst
Risk Analysts assess and manage risks that may affect an organization's operations, finances, or reputation. They use a variety of tools and techniques, including machine learning, to identify and mitigate risks. This course can help Risk Analysts learn more about machine learning and how to use it to improve their risk analysis process. In particular, the course's focus on hyperparameter tuning will be helpful for Risk Analysts who wish to improve the performance of their machine learning models.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines that collect, clean, and transform data. They use a variety of tools and techniques, including machine learning, to ensure that data is accurate, reliable, and available for analysis. This course can help Data Engineers learn more about machine learning and how to use it to improve their data engineering process. In particular, the course's focus on hyperparameter tuning will be helpful for Data Engineers who wish to improve the performance of their machine learning models.
Software Developer
Software Developer
Software Developers design, develop, and maintain software applications. They use a variety of programming languages and tools to create software that meets the needs of users. This course can help Software Developers learn more about machine learning and how to use it to develop software applications. In particular, the course's focus on hyperparameter tuning will be helpful for Software Developers who wish to improve the performance of their machine learning models.
For more career information including salaries, visit:
OpenCourser.com/course/skinbn/hyperparameter
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
Hyperparameter Tuning with Neural Network Intelligence.
Provides a comprehensive and authoritative introduction to machine learning, covering the fundamental concepts, algorithms, and applications.
Provides a comprehensive and rigorous introduction to statistical learning, covering the fundamental concepts, algorithms, and theory.
Save
Provides a comprehensive and rigorous introduction to pattern recognition and machine learning, covering the fundamental concepts, algorithms, and applications.
Provides a practical guide to Keras, a popular Python library for deep learning.
Practical guide to deep learning using Keras, a popular Python library.
Provides a practical and hands-on introduction to machine learning using popular Python libraries.
Provides a comprehensive overview of the state-of-the-art in AI in healthcare, covering foundational concepts, applications, and ethical considerations.
Provides a comprehensive introduction to neural networks and deep learning, covering the fundamental concepts, algorithms, and applications.
Provides a gentle introduction to machine learning, making it accessible to beginners with no prior knowledge.
Provides a visual and intuitive introduction to deep learning, making it accessible to a wide audience.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/skinbn/hyperparameter
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Amit Yadav
Language
English
Good to know
Emphasizes the use of Microsoft's Neural Network Intelligence (NNI) toolkit for hyperparameter tuning, which aligns with industry best practices
Provides hands-on experience in hyperparameter tuning using a real-world dataset, fostering practical skills for learners
Introduces the open source NNI toolkit, enhancing learners' knowledge of available tools in the field
Assumes familiarity with Python, TensorFlow, and Keras, indicating it may be suitable for learners with prior knowledge in deep learning
Focuses on hyperparameter tuning for a neural network, which may not cover other aspects of machine learning model development
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