Deep Learning Essentials

In this course, you learn the essentials of Deep Learning. We start with a brief introduction and illustrate how to set up your software environment. We then review the foundations of artificial neural networks such as the perceptron and multilayer perceptron (MLP) networks. Next, we elaborate on convolutional neural networks illustrated with various examples. We then discuss representational learning and embeddings. Recurrent neural networks are also covered again extensively illustrated with examples. This is followed by a discussion on generative adversarial networks. The course concludes by discussing reinforcement learning.

The course provides a sound mix of both theoretical and technical insights, as well as practical implementation details. These are illustrated by several real-life case studies and examples using Keras and TensorFlow.

The course features 28 Jupyter notebooks containing hands-on examples. A Python tutorial is also provided.

The course features more than 4 hours of video lectures, various multiple choice questions, and lots of references to background literature. A certificate signed by the instructors is provided upon successful completion.