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Pose estimation, in the context of artificial intelligence (AI) and machine learning (ML), is the process of determining the spatial position and orientation (pose) of an object or multiple objects within an image or a video frame. It involves locating key points or landmarks on objects and estimating their positions
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Semantic segmentation is a type of computer vision task that involves assigning each pixel in an image to a semantic class, such as "person," "car," "tree," or "building." This is in contrast to image classification, which assigns a single class label to
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Localization and object detection are computer vision tasks within the field of artificial intelligence (AI) and machine learning (ML) that involve identifying and precisely locating objects within an image or video. While they share similarities, these tasks have distinct goals: 1. Object Detection: Goal: The primary goal of object detection
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Word embeddings are a type of word representation used in artificial intelligence (AI) and machine learning (ML) to convert words or tokens into numerical vectors. These numerical vectors capture semantic information about words, allowing machines to understand and process human language more effectively. Word embeddings are a crucial component in
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Sequence modeling is a type of machine learning that is used to learn from and predict sequential data. Sequential data is data that has a natural order, such as text, audio, video, and time-series data. Sequence modeling is a subfield of artificial intelligence (AI) and machine learning (ML) that focuses
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Autoencoders are a class of artificial neural networks used in artificial intelligence (AI) and machine learning (ML) for various purposes, including dimensionality reduction, feature learning, data compression, and generative modeling. They belong to the family of unsupervised learning algorithms and are designed to encode input data into a lower-dimensional representation
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Generative Artificial Intelligence (also Generative AI or Gen AI) is artificial intelligence capable of generating text, images, or other media, using generative models. Generative AI models advanced in transformer-based deep neural networks enabled a number of generative AI systems notable for accepting natural language prompts as input. Recent successful outcomes
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Artificial Intelligence (AI) can be categorized into several types based on various criteria. Here are some common ways to classify AI: Based on Capabilities: 1. Narrow or Weak AI (ANI): This type of AI is designed for a specific task and can't perform tasks beyond its programmed capabilities.
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Deep learning is a subfield of artificial intelligence (AI) and machine learning (ML) that focuses on the development and application of neural networks, which are computational models inspired by the structure and function of the human brain. It is particularly well-suited for tasks involving large amounts of data, such as
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Reinforcement Learning (RL) is a type of machine learning within the field of artificial intelligence (AI) that focuses on training agents to make a sequence of decisions in an environment in order to achieve a specific goal. It is a learning paradigm inspired by behavioral psychology, where agents learn by
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Semi-supervised machine learning is a type of machine learning paradigm that falls between supervised learning and unsupervised learning. In semi-supervised learning, the training dataset contains a combination of labeled and unlabeled data. This approach is particularly useful when obtaining a large amount of labeled data is costly or time-consuming, but
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Unsupervised machine learning is a category of machine learning where the algorithm is trained on unlabeled data, meaning that it's given input data without any corresponding output or target variable. Unlike supervised learning, where the algorithm learns to make predictions based on labeled data (input-output pairs), unsupervised learning