Computer Vision
A computer vision technique that partitions a digital image into discrete groups of pixels—image segments—to inform object detection and related tasks.
Image Segmentation
Very large deep learning models that are pre-trained on vast amounts of data. The underlying transformer is a set of neural networks that consist of an encoder and a decoder with self-attention capabilities.
Large Language Models (LLMs)
A category of machine learning that uses labeled datasets to train algorithms to predict outcomes and recognize patterns. Unlike unsupervised learning, supervised learning algorithms are given labeled training to learn the relationship between the input and the outputs.
Supervised Learning
The grouping of unlabeled examples. It relies on unsupervised learning.
Clustering
A Python library used for working with data sets. It has functions for analyzing, cleaning, exploring, and manipulating data.
Pandas Library
The method of reducing the input variable to your model by using only relevant data and getting rid of noise in data.
Feature selection
General-purpose language models that can perform a broad range of tasks from creating original content to write code, summarizing text, and extracting data from documents.
Generative Pre-trained Transformer (GPT)
A type of machine learning that learns from data without human supervision. Unlike supervised learning, unsupervised machine learning models are given unlabeled data and allowed to discover patterns and insights without any explicit guidance or instruction.
Unsupervised Learning
A comprehensive library for creating static, animated, and interactive visualizations in Python.
Matplotlib Library
A computer vision technique for locating instances of objects in images or videos. This technique typically leverages machine learning or deep learning to produce meaningful results.
Object detention
A combination of supervised and unsupervised learning. It uses a small amount of labeled data and a large amount of unlabeled data, which provides the benefits of both unsupervised and supervised learning while avoiding the challenges of finding a large amount of labeled data.
Semisupervised Learning
A machine learning technique that trains software to make decisions to achieve the most optimal results. It mimics the trial-and-error learning process that humans use to achieve their goals.
Reinforcement Learning