Synthetic Data
Artificially generated data that mimics real data, used for training machine learning models. Crucial for training models when real data is scarce or sensitive.
20 topics found for:
“machine learning training”
RLHF
Reinforcement Learning from Human Feedback (RLHF) is a machine learning technique that uses human input to guide the training of AI models. Essential for improving the alignment and performance of AI systems in real-world applications.
AI Pre-Training
The process of training an AI model on a large dataset before fine-tuning it for a specific task. Crucial for building robust AI models that perform well on various tasks.
Pre-Trained Transformer
An AI model that has been pre-trained on a large dataset and can be fine-tuned for specific tasks. Essential for developing state-of-the-art NLP applications.
Train/Test
A method of splitting a dataset into two subsets: one for training a model and another for testing its performance. Fundamental for developing and evaluating machine learning models in digital product design.
Machine Learning
A type of artificial intelligence that enables systems to learn from data and improve over time without being explicitly programmed. Crucial for developing intelligent systems that can make data-driven decisions.
Generative Modeling
The use of algorithms to generate new data samples that resemble a training dataset, often used in AI for creating realistic outputs. Important for developing creative and innovative solutions in digital product design, such as content generation and simulation.
Negative Prompt
In AI and machine learning, a prompt that specifies what should be avoided or excluded in the generated output, guiding the system to produce more accurate and relevant results. Crucial for refining AI-generated content by providing clear instructions on undesired elements, improving output quality and relevance.
Neural Network
A set of algorithms, modeled loosely after the human brain, designed to recognize patterns and perform complex tasks. Essential for developing advanced AI applications in various fields.
Product Enablement
The strategies and tools used to ensure that sales, marketing, and customer service teams have the necessary resources to effectively promote and support a product. Essential for aligning internal teams and ensuring successful product adoption and customer satisfaction.
Cross Validation
A statistical method used to assess the generalizability of a model to unseen data, involving partitioning a dataset into subsets for training and validation. Essential for evaluating model performance and preventing overfitting in digital product analytics.
KOS
Knowledge Organization System (KOS) refers to a structured framework for organizing, managing, and retrieving information within a specific domain or across multiple domains. Essential for improving information findability, enhancing semantic interoperability, and supporting effective knowledge management in digital environments.
Hallucination
In AI, the generation of incorrect or nonsensical information by a model, particularly in natural language processing. Important for understanding and mitigating errors in AI systems.
ART
Agile Release Train (ART) is a long-lived team of Agile teams that, along with other stakeholders, incrementally develops, delivers, and operates one or more solutions in a value stream. Important for coordinating Agile development and delivery at scale.
Selection Bias
A bias that occurs when the sample chosen for a study or survey is not representative of the population being studied, affecting the validity of the results. Important for ensuring the accuracy and reliability of research findings and avoiding skewed data.
Data Science
An interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. Essential for driving data-informed decision making, predicting trends, and uncovering valuable insights in digital product design and development.
CSM
CSM (Customer Success Management) is a business methodology focused on ensuring customers achieve their desired outcomes while using a product or service. Crucial for driving customer retention and satisfaction.
UCD
User-Centered Design (UCD) is an iterative design approach that focuses on understanding users' needs, preferences, and limitations throughout the design process. Crucial for creating products that are intuitive, efficient, and satisfying for the intended users.
Program Increment
A time-boxed period in which Agile teams deliver incremental value in the form of working, tested software and systems. Essential for aligning teams, managing dependencies, and ensuring continuous delivery.
Normal Data Distribution
A symmetrical, bell-shaped distribution of data where most observations cluster around the mean. Fundamental in statistics and crucial for many analytical techniques used in digital product design and data-driven decision making.