Browse all content tagged with Machine Learning
Explore the basics of AI reasoning, including its types, importance, and real-world applications. Learn how AI mimics human thought, enhances decision-making, and the challenges of bias and fairness in advanced models like OpenAI’s o1.
Discover the importance and applications of Human in the Loop (HITL) in AI chatbots, where human expertise enhances AI systems for improved accuracy, ethical standards, and user satisfaction across various industries.
Find out what is unstructured data and how it compares to structured data. Learn about the challenges, and tools used for unstructured data.
Unsupervised learning is a branch of machine learning focused on finding patterns, structures, and relationships in unlabeled data, enabling tasks like clustering, dimensionality reduction, and association rule learning for applications such as customer segmentation, anomaly detection, and recommendation engines.
Unsupervised learning is a machine learning technique that trains algorithms on unlabeled data to discover hidden patterns, structures, and relationships. Common methods include clustering, association, and dimensionality reduction, with applications in customer segmentation, anomaly detection, and market basket analysis.
Fastai is a deep learning library built on PyTorch, offering high-level APIs, transfer learning, and a layered architecture to simplify neural network development for vision, NLP, tabular data, and more. Developed by Jeremy Howard and Rachel Thomas, Fastai is open-source and community-driven, making state-of-the-art AI accessible for everyone.
OpenAI Whisper is an advanced automatic speech recognition (ASR) system that transcribes spoken language into text, supporting 99 languages, robust to accents and noise, and open-source for versatile AI applications.
Word embeddings are sophisticated representations of words in a continuous vector space, capturing semantic and syntactic relationships for advanced NLP tasks like text classification, machine translation, and sentiment analysis.
Explainable AI (XAI) is a suite of methods and processes designed to make the outputs of AI models understandable to humans, fostering transparency, interpretability, and accountability in complex machine learning systems.
XGBoost stands for Extreme Gradient Boosting. It is an optimized distributed gradient boosting library designed for efficient and scalable training of machine learning models, known for its speed, performance, and robust regularization.
Zero-Shot Learning is a method in AI where a model recognizes objects or data categories without having been explicitly trained on those categories, using semantic descriptions or attributes to make inferences. It's especially useful when collecting training data is challenging or impossible.
