Reinforcement Learning
Reinforcement Learning is a machine learning algorithm that enables computers to make decisions in dynamic and potentially complex environments. It is a subfield of machine
Category: Lexicon
Reinforcement Learning from Human Feedback
Reinforcement learning is a discipline within Machine Learning where an agent (RL agent) learns its actions (that sometimes includes inaction) through real-time interactions with its
Semantic Segmentation
Semantic segmentation involves the categorization of images at a pixel level. It is best described as classifying particular classes of images and differentiating them from
Sentiment Analysis
Sentiment Analysis is the interpretation of the emotional tone from a written text using advanced text analysis techniques. These techniques are categorized into positive, neutral,
Supervised Learning
Supervised Machine Learning is an algorithm using labeled training data to enable the prediction of unlabeled data. This technique, combined with unsupervised and reinforcement learning,
Tensor Processing Unit (TPU)
Developed by Google, Tensor Processing Units (TPUs) are proprietary processing units facilitating machine learning and neural network projects. Combined with GPUs and CPUs, tensor processing
Test Data
Once the model has been trained using the training dataset, it is essential to test its performance with a test dataset. This dataset evaluates the
Text Analytics
Text Analytics is a branch of AI that uses NLP to create structured data by converting unstructured text from documents and databases. This data is
Training Data
Training-validation-testing data is a set of data fed to a machine learning model to create the model and teach it how to accurately perform a
Unawareness (to a sensitive attribute)
Unawareness of sensitive characteristics is a common issue when constructing models, as these attributes may be omitted from the training data. However, due to correlations
Unsupervised Learning
Machine learning utilizes unsupervised learning as a method for data processing. This form of learning enables systems to identify and analyze unknown data without outside
Validation Data
Data validation is an important step in model development, ensuring the accuracy and quality of the data before training. It serves to identify anomalies that
