The Machine Learning Glossary
Every definition in machine learning, deep learning, computer vision, and NLP
Annotation means any extra information that is attached to the data.
Artificial Neural Network
Artificial Neural Network⤢
Artificial Neural Network is an algorithm that works in a similar fashion as the human brain processes and analyzes data.
In data analysis refers to approaches for increasing the amount of data by adding slightly changed copies of current data or creating new synthetic data from an existing dataset.
In deep learning, backpropagation is one of the two sub-processes of the training process, which adjust the parameters of the forward propagation with respect to the error it produces.
Bounding Box is used in object detection in which the objects are wrapped around an imaginary rectangle.
COCO is an image dataset composed of 90 different classes of objects (cars, persons, sports balls, bicycles, dogs, cats, horses e.t.c). The dataset was gathered to solve common object detection problems.
Computer Vision is a field of Artificial Intelligence that focuses on developing techniques that help computers see and understand the content of digital images.
Convolutional Neural Networks
Convolutional Neural Networks⤢
Convolutional Neural Networks also known as ConvNets are a type of Feed-Forward Neural networks used in tasks like image analysis, natural language processing, or other complex image classification problems.
Dataset is a collection of meaningful data which the machine sees and learns.
Deep Learning is a sub-field of machine learning that works in a manner inspired by the neurons of the brain.
Ensemble Learning is a method of reaching a consensus in predictions by fusing the salient properties of two or more models.
In deep learning, feedforward propagation is one of the two sub-processes of the training process, which builds correlation by assigning parameters.
Image Preprocessing are the steps we take to convert a raw image into an enhanced form that the model is ready to use for training and inference.
Image recognition is the ability of the machine to identify objects, places, people in an image.
Image segmentation is a subfield of Artificial Intelligence and Computer Vision typically used to locate objects and boundaries in images.
Instance segmentation models classify pixels into categories on the basis of “instances” rather than classes.
Machine Learning is a branch of Artificial Intelligence that allows computers to imitate humans in decision-making without being explicitly programmed.
Natural Language Processing
Natural Language Processing⤢
Natural Language Processing refers to the branch of Artificial Intelligence that gives machines the ability to read, understand and derive meaning from languages, just like humans do.
Neuron is the basic computational unit of a neural network.
Object detection is a technology that includes computer vision and image processing used to detect objects of a certain class in images or videos.
Object tracking is a deep learning process where the algorithm tracks the movement of an object. In other words, it is the task of estimating or predicting the positions and other relevant information of moving objects in a video.
Overfitting refers to the model that models the training data way too well.
Panoptic segmentation can be expressed as the combination of semantic segmentation and instance segmentation where each instance of an object in the image is segregated and the object’s identity is predicted.
Polygons are a type of image annotation method, particularly effective thanks to the ability to create a mask around the desired object at a pixel level.
Reinforcement Learning is a type of machine learning algorithm that learns to solve a multi-level problem by trial and error.
Semantic Segmentation involves a finer approach of labeling each pixel of an image with the class of its enclosing object or region.
Supervised Learning is the machine learning approach defined by its use of labeled datasets to train algorithms to classify data and predict outcomes.
Test data is a set of data that is used to test the model after complete training has been done.
Training Data is a set of data that is fed to any machine learning algorithm for it to learn and derive patterns and use this knowledge for further predictions.
Underfitting occurs when we have a high bias in our data, i.e., we are oversimplifying the problem, and as a result, the model does not work correctly in the training data.
Unsupervised Learning is a type of machine learning in which the algorithms are provided with data that does not contain any labels or explicit instructions on what to do with it.
Validation Data is a set of data apart from the training data that is used to validate the model during training.