IBM Coursera Course 1: Generative AI: Introduction and Applications

Natural language processing (NLP)

A branch of artificial intelligence that enables computers to understand, manipulate and generate human language (natural language).

Transformers

A deep learning architecture that uses an encoder-decoder mechanism. Transformers can generate coherent and contextually relevant text.

Generative pre-trained transformer (GPT)

A series of large language models developed by OpenAI. They are designed to understand language by leveraging a combination of two concepts: training and transformers.

Deep learning

A subset of machine learning that focuses on training computers to perform tasks by learning from data. It uses artificial neural networks.

Data augmentation

A technique commonly used in machine learning and deep learning to increase the diversity and amount of training data.

Generative AI

A type of artificial intelligence that can create new content, such as text, images, audio, and video.

Discriminative AI

A type of artificial intelligence that distinguishes between different classes of data.

Machine learning

A type of artificial intelligence that focuses on creating algorithms and models that enable computers to learn and make predictions or decisions. It involves designing systems that can learn from training data.

Large language models (LLMs)

A type of deep learning model trained on massive amounts of text data to learn the patterns and structures of language. They can perform language-related tasks, including text generation, translation, summarization, sentiment analysis, and more.

Generative adversarial network (GAN)

A type of generative model that includes two neural networks: generator and discriminator. The generator is trained on vast data sets to create samples like text and images. The discriminator tries to distinguish whether the sample is real or fake.

Variational autoencoder (VAE)

A type of generative model that is basically a neural network model designed to learn the efficient representation of input data by encoding it into a smaller space and decoding back to the original space.

Diffusion model

A type of generative model that is popularly used for generating high-quality samples and performing various tasks, including image synthesis. They are trained by gradually adding noise to an image and then learning to remove the noise. This process is called diffusion.

Foundation models

AI models with broad capabilities that can be adapted to create more specialized models or tools for specific use cases.

Neural networks

Computational models inspired by the structure and functioning of the human brain. They are a fundamental component of deep learning and artificial intelligence.

Training data

Data (generally, large datasets that also have examples) used to teach a machine learning model.

Prompt

Instructions or questions that are given to a generative AI model to generate new content.

Generative AI models

Models that can understand the context of input content to generate new content. In general, they are used for automated content creation and interactive communication.

Discriminative AI models

Models that identify and classify based on patterns they observe in training data. In general, they are used in prediction and classification tasks.