Summary

With this, we have reached the end of the chapter. You should now understand the evolution of NLP methods and approaches, from BoW to Transformers. We looked at how to implement BoW, RNN, and CNN-based approaches and understood what Word2vec is and how it helps improve the conventional DL-based methods using shallow TL. We also investigated the foundation of the Transformer architecture, with BERT as an example. We learned about TL and how it is utilized by BERT. We also described the general idea behind multimodal learning and provided a quick introduction to ViT. Models such as CLIP and Stable Diffusion were also described.

At this point, we have learned the basic information that is necessary to continue to the next chapters. We understand the main idea behind Transformer-based architectures and how TL can be applied using this architecture.

In the next chapter, we will see how it is possible to run a simple Transformer example from scratch. The related information about the installation steps will be given, and working with datasets and benchmarks will also be investigated in detail.