Now, some general “learning” lingo...
Machine Learning (ML)
A subset of AI that focuses on algorithms and models, enabling computers to learn from data, make predictions and take actions without explicit programming. ML algorithms can be applied to various AI tasks, such as image recognition, natural language processing, recommendation systems and many others. Through ML, AI systems can autonomously improve their performance and adapt to new data or situations.
It's worth noting that ML is just one approach to achieving AI; there are other techniques and branches within AI - such as rule-based systems, expert systems and genetic algorithms - that do not rely on ML.
A subset of ML that leverages artificial neural networks inspired by the human brain. It involves training models with multiple layers to learn complex patterns and representations, enabling breakthroughs in areas like image and speech recognition.
Large Language Model (LLM)
Refers to AI models that can generate natural language texts from large amounts of data. LLMs use deep neural networks, such as transformers, to learn from billions or trillions of words, and to produce texts on any topic or domain.
An approach in machine learning to transfer learning in which the weights of a pre-trained model are trained on new data.
A relatively low-dimensional space into which you can translate high-dimensional vectors. Embeddings make it easier to do machine learning on large inputs like sparse vectors representing words.