Explanation: Generative AI is a branch of artificial intelligence that focuses on creating new content or data based on the patterns and structure of existing data. Unlike other AI approaches that aim to recognize, classify, or predict data, generative AI aims to generate data that is realistic, diverse, and novel. Generative AI can produce various types of content, such as images, text, audio, video, software code, product designs, and more. Generative AI uses different techniques and models to learn from data and generate new examples, such as generative adversarial networks (GANs), variational autoencoders (VAEs), diffusion models, and foundation models. Generative AI has many applications across different domains and industries, such as art, entertainment, education, healthcare, engineering, marketing, and more. Reference: : Oracle Cloud Infrastructure AI - Generative AI, Generative artificial intelligence - Wikipedia Which type of machine learning is used for already labeled data sets?
D. Multi -Class Classification Explanation: Multi -class classification is a type of supervised learning algorithm that is required in this scenario because the output variable has more than two classes. Multi -class classification is the problem of classifying instances into one of three or more classes. For example, classifying patients into low risk, moderate risk, or high risk based on their medical history and vital signs is a multi -class classification problem because each patient can only belong to one of these three classes. Multi -class classification can be solved by using various algorithms, such as decision trees, random forests, support vector machines (SVMs), k -nearest neighbors (k -NN), naive Bayes, logistic regression, neural networks, etc. Some of these algorithms can naturally handle multi -class problems, while others need to be adapted by using strategies such as one -vs-one or one -vs-rest. Reference: : Multiclass classification - Wikipedia, Multiclass Classification - Explained in Machine Learning What is the difference between classification and regression in Supervised Machine Learning?
Explanation: Unsupervised learning is a type of machine learning that is used to understand relationships within data and is not focused on making predictions or classifications. Unsupervised learning algorithms work with unlabeled data, which means the data does not have predefined categories or outcomes. The goal of unsupervised learning is to discover hidden patterns, structures, or features in the data that can provide valuable insights or reduce complexity. Some of the common techniques and applications of unsupervised learning are: Clustering: Grouping similar data points together based on their attributes or distances. For example, clustering can be used to segment customers based on their preferences, behavior, or demographics. Dimensionality reduction: Reducing the number of variables or features in a dataset while preserving the essential information. For example, dimensionality reduction can be used to compress images, remove noise, or visualize high -dimensional data in lower dimensions. Anomaly detection: Identifying outliers or abnormal data points that deviate from the normal distribution or behavior of the data. For example, anomaly detection can be used to detect fraud, network intrusion, or system failure. Association rule mining: Finding rules that describe how variables or items are related or co -occur in a dataset. For example, association rule mining can be used to discover frequent itemsets in market basket analysis or recommend products based on purchase history. Reference: : Unsupervised learning - Wikipedia, What is Unsupervised Learning? | IBM What is the primary purpose of reinforcement learning?
based on the current policy. Q-learning: A popular reinforcement learning algorithm that learns a value function called Q- function, which represents the quality of taking a certain action in a certain state. Deep reinforcement learning: A branch of reinforcement learning that combines deep neural networks with reinforcement learning algorithms to handle complex and high -dimensional problems, such as playing video games or controlling robots. Reference: : Reinforcement learning - Wikipedia, What is Reinforcement Learning? – Overview of How it Works - Synopsys Which AI domain is associated with tasks such as identifying the sentiment of text and translating text between languages?
C. Speech Processing D. Natural Language Processing Explanation: Computer Vision is an AI domain that is associated with tasks such as recognizing faces in images and classifying objects. Computer vision is a field of artificial intelligence that enables computers and systems to derive meaningful information from digital images, videos, and other visual inputs, and to take actions or make recommendations based on that information. Computer vision works by applying machine learning and deep learning models to visual data, such as pixels, colors, shapes, textures, etc., and extracting features and patterns that can be used for various purposes. Some of the common techniques and applications of computer vision are: Face recognition: Identifying or verifying the identity of a person based on their facial features. Object detection: Locating and labeling objects of interest in an image or a video. Object recognition: Classifying objects into predefined categories, such as animals, vehicles, fruits, etc. Scene understanding: Analyzing the context and semantics of a visual scene, such as the location, time, weather, activity, etc. Image segmentation: Partitioning an image into multiple regions that share similar characteristics, such as color, texture, shape, etc. Image enhancement: Improving the quality or appearance of an image by applying filters, transformations, or corrections. Image generation: Creating realistic or stylized images from scratch or based on some input data, such as sketches, captions, or attributes. Reference: What is Computer Vision? | IBM, Computer vision - Wikipedia Which AI task involves audio generation from text?
➢ TOTAL QUESTIONS:300 What is the key feature of Recurrent Neural Networks (RNNs)?
