PyTorch for Deep Learning

It is designed as an intermediate course, so it is not the first introduction to programming or machine learning. You should have working knowledge of Python, machine learning with Python, basic deep learning, and PyTorch. A high-school-level mathematics background is also recommended for knowledge.

Logistic regression provides an important foundation for understanding classification. You will explore how it works with cross-entropy loss and implement it using PyTorch for binary classification tasks. Its detailed understanding makes the transition to more complex neural networks easier.

Softmax regression is an extension of logistic regression used for multi-class classification. It works by converting a model's raw outputs into probabilities across multiple classes, so each input can be assigned to the most likely category. It's important because many real-world problems, like image recognition or text categorization, involve more than two possible outcomes, and softmax regression provides a straightforward way to handle that.

In this course, you will learn how the softmax function converts model outputs into values that can be interpreted across multiple classes. You will then implement SoftMax regression in PyTorch, giving you a practical foundation and experience for tackling multi-class classification problems.

Yes. You will explore what happens inside neural networks. The course covers activation functions, backpropagation, weight initialization, dropout, and gradient descent with momentum. These concepts help you understand how networks adjust their parameters that can improve learning and generalization.

Yes. You will move from shallow networks into deep neural networks, exploring architectures with multiple hidden layers. You will also learn techniques such as dropout, batch normalization, weight initialization, and momentum. You also understand how regularization supports better model performance.

Yes. The course introduces convolution, pooling, multiple input and output channels, and CNN architectures. You will implement convolutional neural networks using PyTorch and explore their application to image classification. You will also learn how GPUs can support demanding deep learning workflows.

Yes. The course concludes with a hands-on Fashion MNIST classification project. You will bring together the concepts covered throughout the course, including model development, fine-tuning, and evaluation. It gives you an opportunity to practically apply your learning to the final project task.

PyTorch gives you a practical framework for developing and experimenting with deep learning models. With an understanding of how models are implemented, this course takes you through progressively more advanced architectures, making it useful for learners building deeper machine learning skills.