Avoid Overfitting Using Regularization in TensorFlow faq

learnersLearners: 15
instructor Instructor: Amit Yadav instructor-icon
duration Duration: duration-icon

Regularization is a powerful tool to prevent overfitting in TensorFlow, and this online course will teach you how to use it effectively, with proven results and a step-by-step approach.

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Course Feature Course Overview Course Provider Discussion and Reviews
Go to class

Course Feature

costCost:

Paid

providerProvider:

Coursera

certificateCertificate:

Paid Certification

languageLanguage:

English

start dateStart Date:

17th Jul, 2023

Course Overview

❗The content presented here is sourced directly from Coursera platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [August 31st, 2023]

What does this course tell?
(Please note that the following overview content is from Alison)
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

We consider the value of this course from multiple aspects, and finally summarize it for you from three aspects: personal skills, career development, and further study:
(Kindly be aware that our content is optimized by AI tools while also undergoing moderation carefully from our editorial staff.)
What skills and knowledge will you acquire during this course?
By taking this 2-hour long project-based course, learners will acquire the skills and knowledge necessary to reduce over-fitting in an image classification problem. Learners will learn the basics of using weight regularization and dropout regularization to create, train, and evaluate a Neural Network model. After the training and regularization, learners will be able to predict image classes of input examples with similar accuracy for both training and validation sets. This course is best suited for learners based in the North America region, however, the same experience is being worked on for other regions.
lHow does this course contribute to professional growth?
This course provides learners with the opportunity to gain a better understanding of how to use weight regularization and dropout regularization to reduce over-fitting in an image classification problem. Through a project-based approach, learners will be able to create, train, and evaluate a Neural Network model that will be able to predict image classes of input examples with similar accuracy for both training and validation sets. This course is beneficial for professional growth as it provides learners with the skills and knowledge necessary to effectively reduce over-fitting in image classification problems.

Is this course suitable for preparing further education?
Yes, this course is suitable for preparing further education. It provides learners with the basics of using weight regularization and dropout regularization to reduce over-fitting in an image classification problem. By the end of the course, learners will have created, trained, and evaluated a Neural Network model that will predict image classes of input examples with similar accuracy for both training and validation sets. This course works best for learners who are based in the North America region, but the same experience is being worked on for other regions.

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faq FAQ for Tensorflow Courses

Q1: Does the course offer certificates upon completion?

Yes, this course offers a paid certificate. AZ Class have already checked the course certification options for you. Access the class for more details.

Q2: How do I contact your customer support team for more information?

If you have questions about the course content or need help, you can contact us through "Contact Us" at the bottom of the page.

Q3: How many people have enrolled in this course?

So far, a total of 15 people have participated in this course. The duration of this course is hour(s). Please arrange it according to your own time.

Q4: How Do I Enroll in This Course?

Click the"Go to class" button, then you will arrive at the course detail page.
Watch the video preview to understand the course content.
(Please note that the following steps should be performed on Coursera's official site.)
Find the course description and syllabus for detailed information.
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Add your desired course to your cart.
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Once in the cart, select the course you want and click "Enroll."
Coursera may offer a Personal Plan subscription option as well. If the course is part of a subscription, you'll find the option to enroll in the subscription on the course landing page.
If you're looking for additional Tensorflow courses and certifications, our extensive collection at azclass.net will help you.

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