Build a Neural Network with PyTorch

In this course, you will be focusing on how PyTorch creates and Neural Network optimizes models. We will quickly iterate through different aspects of PyTorch Neural Networks, giving you strong foundations and all the prerequisites you need to build deep learning models. Designed for students and professionals interested in machine learning and deep learning, this course offers a comprehensive understanding of the theory and practical applications of building and deploying neural networks. Note, this course is a part of a PyTorch Learning Path, check Prerequisites section.

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.

Syllabus
Module 1 – Neural Networks
Introduction to Networks
Network Shape: Depth vs Width
Back Propagation
Activation Functions
Module 2- Deep Networks
Dropout
Initialization
Batch Normalization
Other Optimization Methods
Prerequisites
Note: this course is a part of the PyTorch Learning Path requires the completion of the following courses:
PyTorch: Tensor, Dataset and Data Augmentation
Linear Regression with PyTorch
Classification with PyTorch
Alternatively, the student must have a good understanding of PyTorch Tensors and DataSets, linear regression, and classification.

Skills Prior to Taking this Course
Basic knowledge of the Python programming language