After completing this course, you will be able to:

Explain the significance of generative AI in various domains.
Differentiate between various generative AI architectures and models.
Describe the use of LLMs for NLP tasks.
Describe the key features and significance of the libraries and tools used in generative AI for language processing.
Use Hugging Face libraries in a Jupyter environment to explore generative AI techniques and build a simple chatbot using the Transformers library.
Explain the tokenization process, tokenization methods, and the use of tokenizers.
Implement tokenization.
Explain how data loaders are used for training generative AI models.
Create an NLP data loader.

M1: Gen AI architecture

1.1. Significance and evolution of generative AI

Gen AI refers to DL models that can generate various types of content such as text, images, audio, 3D objects, and music.
These models generate contextually relevent text. An example of a text generation model is GPI.
These models can generate images from text input and seed images. Examples of models for image generation are DALL-E and GAN.
You can use these models to generate natural-sounding speech and text2speech synthesis. An example of this type of model is WaveNet.
Generative AI has specfic applications in industries such as healthcare, finance, gaming, and IT.