This module introduces classification techniques using PyTorch, focusing on Softmax Regression and its implementation. Students learn how to build and train multiclass classifiers using PyTorch.

This module covers fundamental concepts of neural networks, including network architecture, backpropagation, and activation functions. Students learn about network shape considerations and implementation details.

This module explores advanced techniques for training deep neural networks, including dropout for regularization, weight initialization methods, batch normalization, and various optimization strategies.

This module introduces fundamental concepts of computer vision networks, including convolution operations, max pooling, and the architecture of convolutional neural networks. Students also learn about pre-trained networks.

This advanced computer vision module covers GPU-accelerated model training, implementing convolutional networks, and leveraging pre-trained networks for computer vision tasks.

This module covers techniques for reducing data dimensionality, including PCA and autoencoders. Students learn about various autoencoder architectures and their applications in transfer learning.

This module allows students to apply their learning through a comprehensive project, demonstrating their understanding of deep learning concepts and PyTorch implementation.