PyTorch: Tensor, Dataset and Data Augmentation

Data preparation plays a crucial role in effectively solving machine learning (ML) problems. PyTorch, a powerful deep learning framework, offers a plethora of tools to make data loading easy. The PyTorch: Tensor, Dataset and Data Augmentation course will provide you with a solid understanding of the basics and core principles of PyTorch, specifically focusing on tensor manipulation, dataset management, and data augmentation techniques.

«PyTorch: Tensor, Dataset and Data Augmentation» course equips you with the essential skills to handle and transform data efficiently for machine learning tasks. In this course, students will delve into the essential aspects of working with tensors in PyTorch. They will learn how to efficiently manipulate tensors, perform mathematical operations, and leverage tensor-based operations for tasks like data preprocessing and model training. Through a series of lectures and hands-on exercises, you will gain a deep understanding of PyTorch’s data loading capabilities, PyTorch Dataset Object and learn how to preprocess and augment data to maximize model performance.

Syllabus
Overview of Tensors
Tensors 1D
Two-Dimensional Tensors
Derivatives in PyTorch
Simple Dataset
Dataset and Data Augmentation
Recommended Skills Prior to Taking this Course
Basic knowledge of Python programming language.