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Data gravity refers to the ability of a body of data to attract applications, services, and other data. The quality and quantity of data will increase over time, thereby attracting more applications and services to connect to this data.

Gradient Accumulation is a mechanism for dividing a batch of samples used to train a neural network into several small batches of samples that are run sequentially.

Model validation is the process of evaluating the performance of a machine learning (ML) model on a dataset separate from the training dataset. It is an important step in the ML model development process because it helps ensure that the model generalizes to new, unseen data and does not overfit to the training data.

Pool-based sampling is a popular active learning method that selects informative examples for labeling. A pool of unlabeled data is created, and the model selects the most informative examples for manual annotation. These labeled examples are used to retrain the model, and the process is repeated.

Bot Frame is used to create robots and define their behaviors.

Model parameters are variables that control the behavior of a machine learning (ML) model. They are often trained on data and make predictions or choices based on new, unforeseen facts. Model parameters are an important part of machine learning models because they have a large impact on the accuracy and performance of the model.

Noise is a term used to describe unwanted or irrelevant information in an image or video. It can be caused by a variety of factors, including sensor noise, compression artifacts, and environmental factors such as lighting conditions and reflections. Noise can severely degrade the quality and clarity of an image or video, and can make it more difficult to accurately analyze or interpret the image content.

Panoptic segmentation is a computer vision task that involves segmenting an image or video into different objects and their respective parts and labeling each pixel with the corresponding class.

In machine learning, Type 2 errors (also called false negatives) occur when a model incorrectly predicts that a specific condition or attribute does not exist when it actually does.

In machine learning, Type 1 errors, also known as false positives (FP), occur when a model incorrectly predicts the presence of a condition or attribute when it actually does not.

A pretrained model is a machine learning (ML) model that has been trained on a large dataset and can be fine-tuned for a specific task. Pretrained models are often used as a starting point for developing ML models, as they provide an initial set of weights and biases that can be fine-tuned for a specific task.

Model accuracy, also known as model precision, is a measure of the ability of a machine learning (ML) model to make predictions or decisions based on data. It is a common metric for evaluating the performance of ML models and can be used to compare the performance of different models or to evaluate the effectiveness of a specific model for a given task.

In the branch of mathematics known as numerical analysis, polynomial interpolation is the process of interpolating a given set of data using a polynomial. In other words, given a set of data (such as data from sampling), the goal is to find a polynomial that passes through these data points.

In the field of machine learning (ML), interpolation is the process of estimating the value of a function or dataset at points between known data points. Interpolation is often used to fill missing values in a dataset or to remove noise or irregularities in the data.

In machine learning (ML), the learning rate is a hyperparameter that determines the step size for updating model parameters during training.

Keypoint is a very common concept in the field of computer vision. A keypoint is a unique or significant point in an image or video that can be used to identify, describe, or match objects or features in a scene.

Mean Average Precision (mAP) is a widely used performance metric in object detection tasks in machine learning.

The lifecycle in machine learning (ML) is the process of developing and deploying ML models to solve real-world problems. It typically involves a series of steps, including data preparation, model training and evaluation, model deployment, model monitoring, and maintenance.

In the field of machine learning (ML), labeling errors refer to incorrect or inaccurate labels assigned to examples in a dataset.

Labels in computer vision are textual or numerical annotations assigned to objects or regions of interest in images or videos.

Intersection over Union (IOU) is a performance metric used to evaluate the accuracy of annotation, segmentation, and object detection algorithms. It quantifies the overlap between the predicted bounding box or segmented area in the dataset and the ground truth bounding box or annotated area.

Instance segmentation is a computer vision technique that identifies and segments individual objects in an image; unlike semantic segmentation, which groups pixels based on semantic meaning (e.g., road, sky, person), instance segmentation distinguishes between multiple instances of the same object class.

In computer vision, a grayscale image represents a scene or object using a range of grayscale shades rather than a full spectrum. Grayscale images are usually created by converting a full-color image into a single-channel image, where the intensity of each pixel is represented by a single value between 0 (black) and 255 (white).

In machine learning, features are the input variables or attributes used to train a model. These features are used to represent the characteristics or attributes of the data being analyzed and are used by the model to make predictions or classifications.