In this course, participants will dive into the fundamentals of neural networks and gain hands-on experience with PyTorch, one of the most popular frameworks for deep learning. Through a series of interactive lectures, coding exercises, and projects, students will develop a solid foundation in building and training neural networks. Throughout the course, participants will have the opportunity to apply their knowledge through hands-on coding exercises. By the end of the course, students will have the skills and confidence to build, train, and deploy neural networks using PyTorch, enabling them to tackle real-world machine learning challenges and contribute to the advancement of AI applications.

Note: this course is a part of the PyTorch Learning Path requires the completion of the following courses:

Alternatively, the student must have a good understanding of PyTorch Tensors and DataSets, linear regression, and classification.