Module Overview
This course teaches the basics of deep learning and building deep neural networks using PyTorch. Students would get practical experience with PyTorch through coding exercises and projects implementing state of the art AI applications such as style transfer and text generation.
Learning Outcomes
Upon successfully completing the course, you will be able to:
Explain how neural networks work and how to train them using data
Use PyTorch to build and train deep neural networks.
Use deep neural networks to build a classifier that can classify images of dogs and cats with state of the art performance
Use convolutional neural networks to build state of the art computer vision models
Use a deep neural network to transfer the artistic style of one image onto another image
Use recurrent neural networks to learn from sequential data such as text or audio.
Train a network that can generate text one letter at a time
Build a recurrent neural network to accurately predict the sentiment of movie reviews
Deploy PyTorch models