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Variational Auto Encoders (VAEs)

Variational Autoencoders (VAEs) are generative models that are used to learn and generate new data samples, typically in the form of images, but they can be applied to other types of data as well. It consists of an encoder and a decoder. The encoder compresses the input data into a lower-dimensional representation, called the latent […]

Unsupervised Learning

Unsupervised learning is a type of machine learning where the model is not given any labeled training data or feedback on its performance. Instead, it’s given a dataset and is asked to learn patterns and relationships within the data on its own. This is in contrast to supervised learning, where the model is given labeled […]

Target Daily Results

Target Daily Results is a term that refers to the goals or objectives that an Artificial Intelligence (AI) system is designed to achieve on a daily basis. These goals can vary widely depending on the specific application of the AI system. They might include things such as improving efficiency, increasing profits, providing better services or […]

Target-Cost Per Optimization Event

The Target-Cost per Optimization Event refers to the desired cost of using Artificial Intelligence (AI) to optimize a specific task or process. This cost may be measured in terms of financial resources, time, or other resources. In the context of AI, optimization refers to the process of improving the efficiency, accuracy, or effectiveness of a […]

Testing Dataset

A Testing Dataset is a set of data that is used to evaluate the performance of an AI model. It is used to determine how well the model is able to make predictions or decisions based on the data it has been trained on. It is usually a smaller, representative data sample in comparison to […]

Training Dataset

A Training Dataset is a collection of data that is used to teach a machine learning model how to perform a particular task. This dataset is used to train the model to recognize patterns and make predictions or decisions based on those patterns. For example, if you want to teach a model to recognize pictures […]

Transfer Learning

Transfer Learning is a machine learning technique that involves taking a pre-trained model developed for a task and adapting it for use on a new and different task. This can be especially useful when the new task is similar to the original task, or when there is a shortage of data or resources to train […]

Supervised Learning

Supervised Learning is a type of machine learning that involves training a machine model on a dataset that has already been labeled or classified with the correct output or response. The computer is then trained to recognize patterns and relationships within the data and use that knowledge to make predictions about new data. Once the […]

Style Transfer

Style Transfer is a process in which the style of one image is applied to another image, creating a new and unique image. It is used by AI to enhance the creative potential of digital images and help people create beautiful and unique works of art. AI algorithms are able to understand the unique characteristics […]

Self-Supervised Learning Framework

Self-Supervised Learning is a type of machine learning in which the model is given a task to perform, but is not explicitly given the correct answers. Instead, the model must figure out how to solve the task on its own, using the input data as a guide. The Self-Supervised Learning Framework allows the model to […]

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