Classification with PyTorch

Designed for students and enthusiasts, this course equips you with the knowledge and practical skills to build powerful and accurate classification models using PyTorch. It offers a hands-on learning experience, allowing you to apply your knowledge through coding exercises and lessons so by the end of the course, you will possess the skills to build, train, and evaluate classification models using PyTorch. “Classification with PyTorch” is a part of a PyTorch Learning Path, check Prerequisites.

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.

Syllabus
In this course we will learn about:
Linear Classifier and Logistic Regression
Logistic Regression Prediction
Bernoulli Distribution Maximum Likelihood Estimation
Logistic Regression Cross Entropy
Softmax Function
Softmax PyTorch

Prerequisites

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

Completion of PyTorch: Tensor, Dataset and Data Augmentation course
Completion of Linear Regression with PyTorch course

Good understanding of PyTorch Tensors, DataSets and Linear Regression

Skills Prior to Taking this Course
Basic knowledge of Python programming language.
Basic knowledge of PyTorch Framework
Familiarity with fundamental concepts of machine learning and deep learning is beneficial but not mandatory.