PyTorch Time Sequence Prediction With LSTM - Forecasting Tutorial faq

learnersLearners: 1
instructor Instructor: Python Engineer instructor-icon
duration Duration: 1.00 duration-icon

This tutorial provides an introduction to time sequence prediction using PyTorch and Long Short-Term Memory (LSTM) models. It begins by creating a sine wave, then moves on to constructing an LSTM model and training loop. Finally, the model is tested on unseen data to evaluate its performance. This tutorial provides a comprehensive overview of the process of time sequence prediction with LSTM models in PyTorch.

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Course Feature Course Overview Course Provider Discussion and Reviews
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Course Feature

costCost:

Free

providerProvider:

Youtube

certificateCertificate:

Paid Certification

languageLanguage:

English

start dateStart Date:

On-Demand

Course Overview

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

Updated in [February 21st, 2023]

PyTorch Time Sequence Prediction With LSTM - Forecasting Tutorial is an online course that introduces the basics of time sequence prediction with Long Short-Term Memory (LSTM) using PyTorch. The course covers topics such as sine wave creation, LSTM model, training loop, and final testing.
Learners of this course will gain an understanding of the fundamentals of time sequence prediction with LSTM and how to use PyTorch to implement it. They will also learn how to create sine waves, build an LSTM model, and train and test the model.
Possible development paths for learners of this course include furthering their knowledge of time sequence prediction with LSTM by exploring other frameworks such as TensorFlow and Keras, or applying their knowledge to real-world problems such as stock market prediction or weather forecasting.
Learning suggestions for learners of this course include exploring other machine learning frameworks such as TensorFlow and Keras, as well as learning more about deep learning and neural networks. Additionally, learners should consider taking courses on data analysis and visualization to gain a better understanding of the data they are working with.

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