In the course Introduction to Deep Learning with PyTorch on Udacity, you will learn the fundamental concepts of deep learning, including neural networks and gradient descent. Through in-class programming exercises, you will implement a neural network using NumPy and train it using gradient descent. You will also build a neural network to predict student admissions. Additionally, you will be introduced to PyTorch and hear from Soumith Chintala, the creator of PyTorch, about its origins, current usage, and its impact on the future of deep learning. Throughout the course, you will work with Jupyter Notebooks to understand the major components of PyTorch and use a pre-trained neural network to create a state-of-the-art image classifier. You will also explore Convolutional Neural Networks for computer vision applications, including training a network to classify dog breeds. Furthermore, you will learn about style transfer using a pre-trained convolutional network to merge the style of one image with the content of another. The course also covers Recurrent Neural Networks for sequential data, such as natural language, and you will implement a network that learns from Tolstoy's Anna Karenina to generate new text. Additionally, you will use PyTorch to implement a recurrent neural network for text classification, specifically predicting the sentiment of movie reviews. Finally, you will learn how to deploy deep learning models with PyTorch, including building a chatbot and compiling the network for deployment in a production environment.