Predictive Analytics with PyTorch faq

instructor Instructor: Janani Ravi instructor-icon
duration Duration: 3.00 duration-icon

This course introduces the use of PyTorch to build predictive models, such as Recurrent Neural Networks and long-memory neurons in text prediction. Participants will learn to evaluate models using the Mean Average Precision @ K metric.

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Pluralsight

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English

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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 use of PyTorch to build various predictive models, using Recurrent Neural Networks, long-memory neurons in text prediction, and evaluating them using a metric known as the Mean Average Precision @ K.
PyTorch 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. In this course, Predictive Analytics with PyTorch, you will see how to build predictive models for different use-cases, based on the data you have available at your disposal, and the specific nature of the prediction you are seeking to make. First, you will start by learning how to build a linear regression model using sequential layers. Next, you will explore how to leverage recurrent neural networks (RNNs) to capture sequential relationships within text data. Then, you will apply such an RNN to the problem of generating names - a typical example of the kind of predictive model where deep learning far out-performs traditional natural language processing techniques. Finally, you will see how a recommendation system can be implemented in several different ways - relying on techniques such as content-based filtering, collaborative filtering, as well as hybrid methods. When you are finished with this course, you will have the skills to build, evaluate, and use a wide array of predictive models in PyTorch, ranging from regression, through classification, and finally extending to recommendation systems.

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?
Through this course, learners will acquire the skills to use PyTorch to build predictive models for various use cases. They will learn how to construct a linear regression model using sequential layers, and how to leverage recurrent neural networks (RNNs) to capture sequential relationships within text data. Additionally, learners will gain the knowledge to apply an RNN to the problem of generating names, and how to implement a recommendation system using content-based filtering, collaborative filtering, and hybrid methods. Furthermore, learners will be able to evaluate and use a wide array of predictive models in PyTorch, ranging from regression to classification and recommendation systems.

How does this course contribute to professional growth?
This course provides learners with the skills to use PyTorch to build predictive models for various use cases. Through the course, learners will gain a comprehensive understanding of linear regression models, recurrent neural networks (RNNs), and a variety of predictive models. This knowledge can be applied to a wide range of professional contexts, from building recommendation systems to generating names. As such, this course contributes to professional growth by providing learners with the skills to apply predictive analytics in their work.

Is this course suitable for preparing further education?
Predictive Analytics with PyTorch is a suitable course for preparing further education. It provides learners with the skills to build predictive models using PyTorch, including linear regression models, recurrent neural networks (RNNs), and a variety of other models. Learners will also gain the ability to evaluate and use these models in a wide range of use cases. This course is an excellent foundation for further study in predictive analytics.

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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 Predictive Analytics 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?

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Q3: How many people have enrolled in this course?

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