Linear Regression with PyTorch
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Master linear regression using PyTorch. Learn how to implement and optimize linear regression models for prediction and analysis with hands-on exercises in this comprehensive course.
At a Glance
Linear regression is one of the most used technique for prediction. This course will give you a comprehensive understanding of linear regression modelling using the PyTorch framework. Equipped with these skills, you will be prepared to tackle real-world regression problems and utilize PyTorch effectively for predictive analysis tasks. It focuses specifically on the implementation and practical application of linear regression algorithms for predictive analysis. Note, this course is a part of a PyTorch Learning Path, find more in the Prerequisites Section.
Course Syllabus
Module 1:
- Linear Classifier and Logistic Regression
- Linear Regression Training
- Gradient Descent and Cost
- PyTorch Slope
- Linear Regression Training
- Stochastic Gradient Descent and the Data Loader
- Mini-Batch Descent
- Optimization in PyTorch
- Training, Validation and Test Split
- Multiple Linear Regression Prediction
- Multiple Output Linear Regression
Prerequisites
- Completion of PyTorch: Tensor, Dataset and Data Augmentation course
Good understanding of PyTorch Tensors and DataSets
Skills Prior to Taking this Course
- Basic knowledge of Python programming language.
- Basic knowledge of PyTorch Framework
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