Throughout the course, students will learn how to construct linear models and implement logistic regression algorithms using PyTorch. They will gain proficiency in making predictions using logistic regression models and understanding the underlying probabilistic interpretation. Students will also delve into Bernoulli distribution maximum likelihood estimation and logistic regression cross-entropy, enabling them to effectively estimate model parameters and optimize them for classification tasks. Furthermore, the course covers the application of the softmax function for multiclass classification, providing students with the necessary knowledge to perform accurate and reliable multiclass classification using PyTorch.

In this course we will learn about:

Note: this course is a part of PyTorch Learning Path and the following is required :

or 

Good understanding of PyTorch Tensors, DataSets and Linear Regression