The purpose of this chapter is to introduce you to transformer models, the core idea of attention, and milestone transformer architectures. We will also cover the tooling used in the transformer ecosystem, including libraries like sbert, transformers, and simpletransformers.
Throughout the course, we will apply transformers to real-world business problems. This will include using sbert for sentence and image embedding, finetuning and inference of common transformer-based language models like BERT, and training timeseries transformers for time-sensitive business applications.
we will use real-world business examples to illustrate the concepts and techniques covered in the lectures. By the end of the course, students will have a solid understanding of transformer models and its current applications in business.
The students will have frequent opportunities to work in groups on four mini-projects. These projects will involve applying the concepts and techniques learned in the lectures to real-world business examples. The goal of these projects is to give students hands-on experience with deep learning and help them build a portfolio of work to showcase their skills. By the end of the course, each student will have a collection of four mini-projects that demonstrate their ability to apply deep learning to solve business problems.