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Avoid Overfitting Using Regularization in TensorFlow
Amit Yadav
Amit Yadav
In this 2-hour long project-based course, you will learn the basics of using weight regularization and dropout regularization to reduce over-fitting in an image classification problem. By the end of this project, you will have created, trained, and evaluated a Neural Network model that, after the training and regularization, will predict image classes of input examples with similar accuracy for both training and validation sets.
Read more
In this 2-hour long project-based course, you will learn the basics of using weight regularization and dropout regularization to reduce over-fitting in an image classification problem. By the end of this project, you will have created, trained, and evaluated a Neural Network model that, after the training and regularization, will predict image classes of input examples with similar accuracy for both training and validation sets.
Read more
In this 2-hour long project-based course, you will learn the basics of using weight regularization and dropout regularization to reduce over-fitting in an image classification problem. By the end of this project, you will have created, trained, and evaluated a Neural Network model that, after the training and regularization, will predict image classes of input examples with similar accuracy for both training and validation sets.
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/c7opnp/avoid
What's inside
Syllabus
TensorFlow Beginner: Avoid Over-fitting Using Regularization
Welcome to this project-based course on Avoid Over-fitting Using Regularization with Keras and TensorFlow. In this project, you will learn the basics of using weight regularization and dropout regularization to reduce over-fitting in an image classification problem. By the end of this 2-hour long project, you will have created, trained, and evaluated a Neural Network model that, after the training and regularization, will predict image classes of input examples with similar accuracy for both training and validation sets.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Engineers and developers with little to no experience with machine learning
Teaches strategies to enhance model accuracy and reduce overfitting
Taught by an experienced instructor in the field of machine learning
Provides a practical approach to applying image classification models
Coursework is designed to be beginner-friendly and easy to follow
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Reviews summary
Tensorflow regularization
Learners say that this course is a good introduction to regularization techniques in TensorFlow. Students describe the example code as clean, simple, and relevant.
The example code is clean.
"It's nice to learn these techniques with a relevant, but simple, example code."
This course is too difficult.
"Not efficiently"
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 Avoid Overfitting Using Regularization in TensorFlow with these
activities:
Review linear algebra and calculus
Show steps
Strengthen your mathematical foundation by reviewing linear algebra and calculus, which are essential for understanding the concepts of neural networks
Browse courses on
Linear Algebra
Show steps
-
Review the basics of linear algebra
-
Review the basics of calculus
Join a study group
Show steps
Foster collaboration, improve understanding, and enhance problem-solving skills through participation in a study group
Show steps
-
Find a study partner or group
-
Set regular meeting times
-
Discuss course materials and concepts
-
Work together on assignments and projects
Deep dive into neural networks
Show steps
Review the basics of neural networks to improve understanding and readiness for this course with a significant focus on neural networks
Browse courses on
Neural Networks
Show steps
-
Review the basics of neural networks
-
Explore different neural network architectures
-
Implement a simple neural network from scratch
Four other activities
Expand to see all activities and additional details
Show all seven activities
Explore TensorFlow tutorials
Show steps
Enhance understanding of course concepts by following guided tutorials and exploring additional resources on TensorFlow
Browse courses on
TensorFlow
Show steps
-
Follow a TensorFlow tutorial on image classification
-
Explore the TensorFlow documentation on regularization techniques
Contribute to an open-source project
Show steps
Gain practical experience and contribute to the community by working on open-source projects related to neural networks or machine learning
Browse courses on
Open Source
Show steps
-
Find an open-source project that interests you
-
Identify a way to contribute to the project
-
Submit a pull request with your contributions
Participate in a hackathon
Show steps
Challenge yourself and apply course concepts in a competitive environment by participating in a hackathon focused on neural networks or machine learning
Show steps
-
Find a hackathon that aligns with your interests
-
Form a team or work individually
-
Develop a solution to the hackathon challenge
-
Present your solution to a panel of judges
Develop a neural network project
Show steps
Apply course concepts by developing a neural network project that addresses a real-world problem
Show steps
-
Identify a problem that can be solved using a neural network
-
Collect and prepare data for the neural network
-
Build and train a neural network model
-
Evaluate and improve the neural network model
-
Deploy the neural network model
Review linear algebra and calculus
Show steps
Strengthen your mathematical foundation by reviewing linear algebra and calculus, which are essential for understanding the concepts of neural networks
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review the basics of linear algebra
- Review the basics of calculus
Join a study group
Show steps
Foster collaboration, improve understanding, and enhance problem-solving skills through participation in a study group
Show steps
Show steps
Show steps
- Find a study partner or group
- Set regular meeting times
- Discuss course materials and concepts
- Work together on assignments and projects
Deep dive into neural networks
Show steps
Review the basics of neural networks to improve understanding and readiness for this course with a significant focus on neural networks
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Review the basics of neural networks
- Explore different neural network architectures
- Implement a simple neural network from scratch
Four other activities
Expand to see all activities and additional details
Show all seven activities
Explore TensorFlow tutorials
Show steps
Enhance understanding of course concepts by following guided tutorials and exploring additional resources on TensorFlow
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Follow a TensorFlow tutorial on image classification
- Explore the TensorFlow documentation on regularization techniques
Contribute to an open-source project
Show steps
Gain practical experience and contribute to the community by working on open-source projects related to neural networks or machine learning
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Find an open-source project that interests you
- Identify a way to contribute to the project
- Submit a pull request with your contributions
Participate in a hackathon
Show steps
Challenge yourself and apply course concepts in a competitive environment by participating in a hackathon focused on neural networks or machine learning
Show steps
Show steps
Show steps
- Find a hackathon that aligns with your interests
- Form a team or work individually
- Develop a solution to the hackathon challenge
- Present your solution to a panel of judges
Develop a neural network project
Show steps
Apply course concepts by developing a neural network project that addresses a real-world problem
Show steps
Show steps
Show steps
- Identify a problem that can be solved using a neural network
- Collect and prepare data for the neural network
- Build and train a neural network model
- Evaluate and improve the neural network model
- Deploy the neural network model
Career center
Learners who complete Avoid Overfitting Using Regularization in TensorFlow will develop knowledge and skills
that may be useful to these careers:
Machine Learning Research Scientist
Machine Learning Research Scientist
Machine Learning Research Scientists develop new machine learning algorithms and techniques. They work on a wide range of projects, from developing new methods for image recognition to natural language processing. This course can provide Machine Learning Research Scientists with a strong foundation in regularization techniques and their applications in real-world machine learning research problems. By understanding how to prevent overfitting, Machine Learning Research Scientists can develop more accurate and robust models.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. They work on a wide range of projects, from clinical trials to public opinion polls. This course can provide Statisticians with a strong foundation in regularization techniques and their applications in real-world data analysis problems. By understanding how to prevent overfitting, Statisticians can develop more accurate and robust models.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, and deploy machine learning models. They work closely with Data Scientists to turn theoretical models into real-world applications. This course can provide Machine Learning Engineers with a strong foundation in regularization techniques and their applications in image classification. By understanding how to prevent overfitting, Machine Learning Engineers can develop more accurate and robust models.
Biostatistician
Biostatistician
Biostatisticians use statistical methods to design and analyze biomedical research studies. They work on a wide range of projects, from clinical trials to genetic studies. This course can provide Biostatisticians with a strong foundation in regularization techniques and their applications in real-world biomedical research problems. By understanding how to prevent overfitting, Biostatisticians can develop more accurate and robust models.
Data Scientist
Data Scientist
Data Scientists are responsible for collecting, analyzing, and interpreting large amounts of data. The insights that they gather from this data can be used to solve various business problems. This course can be a valuable tool for aspiring Data Scientists by providing them with the skills needed to clean and process data, as well as to build and train machine learning models. The course's focus on regularization techniques can help Data Scientists to improve the accuracy and generalization of their models, which is critical for making reliable predictions.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve complex business problems. They work on a wide range of projects, from supply chain management to healthcare optimization. This course can provide Operations Research Analysts with a strong foundation in regularization techniques and their applications in real-world problems. By understanding how to prevent overfitting, Operations Research Analysts can develop more accurate and robust models.
Data Science Manager
Data Science Manager
Data Science Managers lead and manage teams of Data Scientists and Data Analysts. They are responsible for developing and implementing data science strategies. This course can provide Data Science Managers with a strong understanding of regularization techniques and their applications in real-world data science projects. By understanding how to prevent overfitting, Data Science Managers can help their teams to develop more accurate and robust models.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk and uncertainty. They work in a variety of industries, including insurance, finance, and healthcare. This course can provide Actuaries with a strong foundation in regularization techniques and their applications in real-world risk assessment problems. By understanding how to prevent overfitting, Actuaries can develop more accurate and robust models.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends and patterns. They use their findings to make recommendations and solve business problems. This course can help Data Analysts to develop the skills needed to build and train machine learning models. The course's focus on regularization techniques can help Data Analysts to improve the accuracy and generalization of their models, which is critical for making reliable predictions.
Business Analyst
Business Analyst
Business Analysts use data and analysis to solve business problems. They work closely with stakeholders to identify and prioritize business needs. This course can provide Business Analysts with a strong foundation in regularization techniques and their applications in real-world business problems. By understanding how to prevent overfitting, Business Analysts can develop more accurate and robust models.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. They play a vital role in making investment decisions and managing risk. This course can be helpful for Quantitative Analysts by providing them with a deeper understanding of regularization techniques. By understanding how to prevent overfitting, Quantitative Analysts can develop more accurate and reliable models.
Financial Analyst
Financial Analyst
Financial Analysts use financial data to make investment recommendations and manage risk. They work with a wide range of clients, from individuals to large institutions. This course can be helpful for Financial Analysts by providing them with a deeper understanding of regularization techniques. By understanding how to prevent overfitting, Financial Analysts can develop more accurate and reliable models.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They work on a wide range of projects, from small personal apps to large enterprise systems. This course can be useful for Software Engineers who want to gain experience in machine learning and artificial intelligence. The course's focus on regularization techniques can help Software Engineers to write more efficient and robust code.
Epidemiologist
Epidemiologist
Epidemiologists study the distribution and patterns of health and disease in populations. They use their findings to develop public health interventions and policies. This course can be useful for Epidemiologists who want to gain experience in machine learning and artificial intelligence. The course's focus on regularization techniques can help Epidemiologists to develop more accurate and reliable models.
Market Researcher
Market Researcher
Market Researchers study consumer behavior and market trends. They use their findings to develop marketing strategies and products. This course can be useful for Market Researchers who want to gain experience in machine learning and artificial intelligence. The course's focus on regularization techniques can help Market Researchers to develop more accurate and reliable models.
For more career information including salaries, visit:
OpenCourser.com/course/c7opnp/avoid
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
Avoid Overfitting Using Regularization in TensorFlow.
Provides comprehensive coverage of machine learning concepts and practical implementation using TensorFlow 2.0. The focus on TensorFlow aligns well with the course. The coverage of regularization techniques is particularly relevant to the course topic.
Provides a comprehensive collection of recipes and code examples for TensorFlow and deep learning, covering topics such as data preprocessing, model building, and deployment.
This guide provides a concise and practical introduction to the fundamentals of TensorFlow 2.0, addressing the core aspects of this machine learning framework used in the course.
Serves as a comprehensive guide to TensorFlow, focusing on its use for machine intelligence applications. It covers a wide range of topics relevant to the course, including model building, training, and optimization.
Offers a visually appealing and engaging introduction to deep learning, making it an excellent resource for those seeking a more intuitive understanding of the concepts and algorithms involved.
Presents deep learning concepts in a highly accessible and intuitive manner, making it an excellent choice for those new to the field or seeking a deeper understanding of its underlying principles.
Provides a practical understanding of building and training deep neural networks, with detailed explanations of how TensorFlow works. If you want to get started with TensorFlow, this great book to have in your library.
Presents a comprehensive overview of deep learning using R, providing an alternative perspective to the course's exclusive focus on TensorFlow. It offers valuable insights for those interested in exploring deep learning with a different programming language.
Is written by the creator of Keras, the high-level neural networks API, written in Python and capable of running on top of TensorFlow. This book provides an approachable and effective introduction to deep learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/c7opnp/avoid
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Amit Yadav
Language
English
Good to know
Engineers and developers with little to no experience with machine learning
Teaches strategies to enhance model accuracy and reduce overfitting
Taught by an experienced instructor in the field of machine learning
Provides a practical approach to applying image classification models
Coursework is designed to be beginner-friendly and easy to follow
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