Most Recent Oracle 1Z0-1122-23 Exam Dumps

Text to speech (TTS) is an AI task that involves audio generation from text. TTS is a technology that converts text into spoken audio using natural sounding voices. TTS can read aloud any text data, such as PDFs, websites, books, emails, etc., and provide an auditory format for accessing written content. TTS can be helpful for anyone who needs to listen to text data for various reasons, such as accessibility, convenience, multitasking, learning, entertainment, etc. TTS uses different techniques and models to generate speech from text data, such as:

Concatenative synthesis: Combining pre-recorded segments of human speech based on the phonetic units of the text.

Parametric synthesis: Generating speech signals from acoustic parameters derived from the text using statistical models.

Neural synthesis: Using deep neural networks to learn the mapping between text and speech features and produce high-quality speech signals.

Expressive synthesis: Adding emotions or styles to the speech output to make it more natural and engaging.Reference::Text-to-Speech AI: Lifelike Speech Synthesis | Google Cloud,Text-to-speech synthesis - Wikipedia

Large language models are trained on massive amounts of data to capture the complexity and diversity of natural language. Larger model sizes mean more parameters, which enable the model to learn more patterns and nuances from the data. Larger models also tend to generalize better to new tasks and domains. However, larger models also require more computational resources, data quality, and data size to train and deploy.Therefore, large language models handle the trade-off by prioritizing larger model sizes to achieve better performance, while using various techniques to optimize the training and inference efficiency4.Reference:Artificial Intelligence (AI) | Oracle

Machine learning is a branch of artificial intelligence that enables computers to learn from data and experience without being explicitly programmed.Machine learning algorithms can adapt to new data and situations and improve their performance over time2.Reference:Artificial Intelligence (AI) | Oracle

Reinforcement learning is a type of machine learning that is based on learning from outcomes to make decisions. Reinforcement learning algorithms learn from their own actions and experiences in an environment, rather than from labeled data or explicit feedback. The goal of reinforcement learning is to find an optimal policy that maximizes a cumulative reward over time. A policy is a rule that determines what action to take in each state of the environment. A reward is a feedback signal that indicates how good or bad an action was for achieving a desired objective. Reinforcement learning involves a trial-and-error process of exploring different actions and observing their consequences, and then updating the policy accordingly. Some of the challenges and components of reinforcement learning are:

Exploration vs exploitation: Balancing between trying new actions that might lead to higher rewards in the future (exploration) and choosing known actions that yield immediate rewards (exploitation).

Markov decision process (MDP): A mathematical framework for modeling sequential decision making problems under uncertainty, where the outcomes depend only on the current state and action, not on the previous ones.

Value function: A function that estimates the expected long-term return of each state or state-action pair, 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

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