Image Classification with PyTorch faq

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Learn the basics of Image Classification with PyTorch

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

Course Feature

costCost:

Free Trial

providerProvider:

Pluralsight

certificateCertificate:

Paid Certification

languageLanguage:

English

start dateStart Date:

On-Demand

Course Overview

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

Updated in [February 21st, 2023]

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

This course covers the parts of building enterprise-grade image classification systems like image pre-processing, picking between CNNs and DNNs, calculating output dimensions of CNNs, and leveraging pre-trained models using PyTorch transfer learning.
Perhaps the most ground-breaking advances in machine learnings have come from applying machine learning to classification problems. In this course, Image Classification with PyTorch, you will gain the ability to design and implement image classifications using PyTorch, which is fast emerging as a popular choice for building deep learning models owing to its flexibility, ease-of-use and built-in support for optimized hardware such as GPUs. First, you will learn how images can be represented as 4-D tensors and then pre-processed to get the best out of ML algorithms. Next, you will discover how to implement image classification using Dense Neural Networks; you will then understand and overcome the associated pitfalls using Convolutional Neural Networks (CNNs). Finally, you will round out the course by understanding and using the most powerful and popular CNN architectures such as VGG, AlexNet, DenseNet and so on, and leveraging PyTorch’s support for transfer learning. When you’re finished with this course, you will have the skills and knowledge to design and implement efficient and powerful image classification solutions using a range of neural network architectures in PyTorch.

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?
This course will provide learners with the skills and knowledge to design and implement efficient and powerful image classification solutions using a range of neural network architectures in PyTorch. Learners will gain an understanding of how images can be represented as 4-D tensors and pre-processed to get the best out of ML algorithms. They will learn how to implement image classification using Dense Neural Networks and Convolutional Neural Networks (CNNs). Learners will also understand and use the most powerful and popular CNN architectures such as VGG, AlexNet, DenseNet and so on, and leverage PyTorch’s support for transfer learning.

How does this course contribute to professional growth?
This course provides learners with the skills and knowledge to design and implement efficient and powerful image classification solutions using a range of neural network architectures in PyTorch. Learners will gain an understanding of how images can be represented as 4-D tensors and pre-processed to get the best out of ML algorithms, as well as how to implement image classification using Dense Neural Networks and Convolutional Neural Networks (CNNs). Additionally, learners will understand and use the most powerful and popular CNN architectures such as VGG, AlexNet, DenseNet and so on, and leverage PyTorch’s support for transfer learning. This course contributes to professional growth by providing learners with the skills and knowledge to design and implement efficient and powerful image classification solutions.

Is this course suitable for preparing further education?
This course is suitable for preparing further education as it provides learners with the skills and knowledge to design and implement efficient and powerful image classification solutions using a range of neural network architectures in PyTorch. Learners will gain an understanding of how images can be represented as 4-D tensors and pre-processed to get the best out of ML algorithms, as well as learn how to implement image classification using Dense Neural Networks and Convolutional Neural Networks (CNNs). Additionally, learners will understand and use the most powerful and popular CNN architectures such as VGG, AlexNet, DenseNet and so on, and leverage PyTorch’s support for transfer learning.

Course Provider

Provider Pluralsight's Stats at AZClass

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AZ Class hope that this free trial Pluralsight course can help your Pytorch skills no matter in career or in further education. Even if you are only slightly interested, you can take Image Classification with PyTorch course with confidence!

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faq FAQ for Pytorch Courses

Q1: Does the course offer certificates upon completion?

Yes, this course offers a free trial 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 0 people have participated in this course. The duration of this course is 4.00 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.
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(Please note that the following steps should be performed on Pluralsight's official site.)
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Once in the cart, select the course you want and click "Enroll."
Pluralsight 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 Pytorch courses and certifications, our extensive collection at azclass.net will help you.

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