The learning outcomes for the Deep Learning - Part 1 certificate are:
The basic algorithms and architecture underlying Deep Learning
Understanding Convolutions and Stochastic Gradient Descent (SGD)
Implementation and mastery of the components of a Convolution Neural Network (CNN)
Applying Deep Learning to problems in computer vision, NLP, tabular data, and collaborative filtering, including state of the art networks such as resnet and unet
Understanding the structure of a Recurrent Neural Network (RNN)
Creating models in PyTorch and fastai.