In this module, you’ll explore two powerful architectures in deep learning: autoencoders and generative adversarial networks (GANs). You’ll begin by learning how autoencoders compress and reconstruct data using encoder-decoder structures, and how reconstruction loss is minimized through backpropagation and gradient descent. You’ll then examine the role of loss functions and optimization techniques in training these models. In the second half of the module, you’ll dive into GANs, where a generator and discriminator compete to produce realistic synthetic data. You’ll study how adversarial training works, how binary cross-entropy loss is applied, and how GANs are used to model complex data distributions. By the end of this module, you’ll be able to implement and evaluate both autoencoders and GANs for representation learning and data generation.

In this module, you’ll learn how convolutional neural networks extract features from images and perform classification. You’ll begin by building a tiny CNN by hand and in Excel, exploring convolution, max-pooling, and fully connected layers. Then, you’ll scale up to larger CNN architectures and examine how they process data through multiple convolution and pooling stages. You’ll also study how categorical cross-entropy loss and gradients are computed for training. Finally, you’ll walk through backpropagation across all CNN layers to understand how learning occurs.

In this module, you’ll explore two influential deep learning architectures: ResNet and U-Net. You’ll begin by learning how ResNet uses skip connections and residual learning to enable the training of very deep networks, addressing challenges like vanishing and exploding gradients. You’ll examine how residual blocks preserve information and support higher-order logic across layers. Then, you’ll shift to U-Net, a powerful architecture for image segmentation, and study its encoder-decoder structure, skip connections, and upsampling techniques like transposed convolution. By the end of this module, you’ll understand how both architectures enhance learning efficiency and performance in complex vision tasks.