2 mins to read Data preparation plays a crucial role in effectively solving machine learning problems. PyTorch, a powerful deep learning framework, offers a plethora of tools to make data loading easy. The PyTorch Tensor, Dataset, and Data Augmentation Fundamentals course provides students 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.
How to Enroll:
- Choose your desired certificate program on the IBM website.
- Create an Amazon if you don’t have one.
- Select specific courses within your chosen program.
- Enroll in courses, and pay if necessary.
- Access course materials and complete requirements.
- Prepare for and take certification exams if required.
- Earn your certificate upon successful completion.
- Be aware of maintenance or renewal requirements, if applicable.