Search Results for "time"

LightAutoML is an automated machine learning (AutoML) framework optimized for efficient model training and hyperparameter tuning, focusing on both tabular and text data.

API-for-Open-LLM is a lightweight API server designed for deploying and serving open large language models (LLMs), offering a simple way to integrate LLMs into applications.

...Generally, the next user interaction is related to the sequence of the user's previous choices. In some cases, it might be a repeated purchase or song play. User interests can also suffer from interest drift because preferences can change over time. Those challenges are addressed by the sequential recommendation task.

Text Generation Inference is a high-performance inference server for text generation models, optimized for Hugging Face's Transformers. It is designed to serve large language models efficiently with optimizations for performance and scalability.

Obsei is an automated no-code/low-code AI-powered text observation and analysis framework, designed for extracting insights from unstructured text data such as social media, reviews, and logs.

...It is edited by three world-renowned experts, Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Includes linear algebra, probability theory, information theory, numerical optimization, and related content in machine learning. At the same time, it also introduces deep learning techniques used by practitioners in the industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling and practical methods, and investigates topics such as natural language processing, Applications in speech recognition, computer vision, online recommender systems, bioinformatics, and video games. ...

VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool designed for analyzing the sentiment of text, particularly in social media and short text formats. It is optimized for quick and accurate analysis of positive, negative, and neutral sentiments.