AI Terminologies — Easy to understand
These are some of the terminologies used in the Machine Learning and Artificial Intelligence space.
Machine Learning
Machine Learning is the science of getting computers to learn, without being explicitly programmed
Artificial Intelligence
Artificial Intelligence is the field of science concerned with building computers and machines that can reason, learn, and act in such a way that would normally require human intelligence or that involves data whose scale exceeds what humans can analyze
Deep Learning
Deep Learning is a subset of Machine Learning that uses multi-layered neural networks called deep neural networks, to simulate the complex-decision making power of the human brain
Large Language Models(LLM)
Also known as LLMs, are very large deep learning models that are pre-trained on vast amounts of data.
Generative AI
Generative AI refers to deep-learning models that can generate high-quality text, images and other content based on the data they were trained on
Prompt Engineering
Prompt Engineering is the process where you guide generative artificial intelligence ( generative AI) solutions to generate desired outputs
Retrieval Augmented Generation(RAG)
A technique for enhancing the accuracy and reliability of generative AI models with facts fetched from external sources. In other words, it fills a gap in how LLMs work.
Generative Pre-trained transformers(GPT)
A family of neural network models in Generative AI applications such as ChatGPT. These models give-applications the ability to create human-like text and content in a conversational manner
Machine Learning Model
A machine learning model is an object ( stored locally in a file) that has been trained to recognize certain types of patterns. You train a model over a set of data, providing it an algorithm that it can use to reason over an learn from those data.
Once you have trained the model, you can use it to reason over data that it hasn’t seen before, and make predictions about those data.
