Most Recent Oracle 1Z0-1122-23 Exam Dumps

Model training is the process of finding the optimal values for the model parameters that minimize the error between the model predictions and the actual output.This is done by using a learning algorithm that iteratively updates the parameters based on the input features and the output1.Reference:Oracle Cloud Infrastructure Documentation

Oracle Cloud Infrastructure Language service is a cloud-based AI service for performing sophisticated text analysis at scale. It provides various capabilities to process unstructured text and extract structured information like sentiment or entities using natural language processing techniques. Some of the capabilities supported by Oracle Cloud Infrastructure Language service are:

Language Detection: Detects languages based on the provided text, and includes a confidence score.

Text Classification: Identifies the document category and subcategory that the text belongs to.

Named Entity Recognition: Identifies common entities, people, places, locations, email, and so on.

Key Phrase Extraction: Extracts an important set of phrases from a block of text.

Sentiment Analysis: Identifies aspects from the provided text and classifies each into positive, negative, or neutral polarity.

Text Translation: Translates text into the language of your choice.

Personal Identifiable Information: Identifies, classifies, and de-identifies private information in unstructured textReference::Language Overview - Oracle,AI Text Analysis at Scale | Oracle

Large language models (LLMs) are a class of deep learning models that can recognize and generate natural language, among other tasks. LLMs are trained on huge sets of text data, learning grammar, semantics, and context. LLMs use the Transformer architecture, which relies on self-attention to process and understand the input and output sequences.LLMs can perform various natural language processing and understanding tasks based on the input provided, such as text summarization, question answering, text generation, and more34. Traditional machine learning models, on the other hand, are usually trained with specific statistical algorithms that deliver pre-defined outcomes.They often require labeled data and feature engineering, and they are not as flexible and adaptable as LLMs5.Reference:What are LLMs, and how are they used in generative AI?,An Introduction to LLMOps: Operationalizing and Managing Large Language Models using Azure ML,An Introduction to Large Language Models (LLMs): How It Got ... - Labellerr

Supervised learning is a type of machine learning that uses labeled data sets to train algorithms that can classify data or predict outcomes. Labeled data sets are data sets that have both input features and output labels for each instance. For example, a labeled data set for image classification would have images as input features and the corresponding categories (such as dog, cat, bird, etc.) as output labels. Supervised learning algorithms learn the relationship between the input features and the output labels from the training data set and then use that relationship to make predictions on new or unseen data. Supervised learning can be divided into two subtypes: classification and regression. Classification is the task of assigning discrete categories to data instances, such as spam or not spam for emails. Regression is the task of predicting continuous values for data instances, such as house prices or stock prices.Reference::Oracle Cloud Infrastructure AI - Machine Learning Concepts,What is Supervised Learning? | IBM

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