In the course Introduction to Deep Learning on Udacity, you will learn about the differences between artificial intelligence, machine learning, and deep learning, and explore various examples of deep learning applications. You will also discover how to minimize the error function using gradient descent and utilize PyTorch to preprocess data and measure model performance. Additionally, you will gain an understanding of neural networks, their essential concepts, and how to design appropriate architectures based on the model objectives. The course will teach you how to train neural networks by defining loss functions and optimization methods, and how to prevent overfitting and underfitting. You will also learn about various optimization techniques such as early stopping, regularization, dropout, learning rate decay, and momentum. Through hands-on exercises, you will build a neural network using PyTorch and test and validate its performance. Finally, you will apply your knowledge to develop a handwritten digit recognition system using PyTorch, including data preprocessing, model development, and advanced training techniques to improve accuracy.