In the course Deep Learning on Udacity, you will start by learning the fundamentals of deep learning and the foundational algorithms of gradient descent and backpropagation. Then, you will explore the design constructs of neural networks and their impact on performance. The course also covers optimization techniques for neural network training to improve accuracy and robustness. Next, in the module Developing a Handwritten Digits Classifier with PyTorch, you will delve into convolutional neural networks (CNNs), which are specialized in image processing. You will learn the advantages of CNNs over standard neural networks for image processing and understand their inner workings. Through transfer learning, you will apply CNN architectures to custom datasets and explore object detection and semantic segmentation using CNNs. Moving on to Landmark Classification and Tagging for Social Media, the course focuses on recurrent neural networks (RNNs) and transformers. You will learn about different RNN architectures and design patterns for these models. Additionally, you will explore the latest transformer architectures. In the module Text Translation and Sentiment Analysis using Transformers, you will learn how to use transformers for tasks like text translation and sentiment analysis. Finally, in Building Generative Adversarial Networks, you will become familiar with generative adversarial networks (GANs) and learn how to build and train different GAN architectures to generate new images. You will work with diverse datasets, including MNIST, Summer2Winter Yosemite, and CelebA, and explore architectures such as DCGAN, CycleGAN, ProGAN, and StyleGAN. Throughout the course, you will gain a comprehensive understanding of deep learning techniques and their applications in various domains, from image processing to natural language processing and image generation.