Best AI tools for< Sem Manager >

iSmart Communications is an AI-powered lead generation agency based in Singapore and Asia. They specialize in leveraging artificial intelligence to revolutionize lead generation strategies for businesses, offering a comprehensive range of AI-powered services such as Inbound Marketing, Account-Based Marketing, Email Marketing, Social Media Marketing, SEO/SEM, and Ecommerce Advertising. The agency focuses on tailoring lead generation strategies to fit the specific characteristics of each Asian market for maximum efficiency, helping businesses accelerate growth and drive remarkable results.

TopMarketingAI.com is a website dedicated to showcasing the power of artificial intelligence in the field of marketing. It offers a variety of free AI tools, such as GPTs and ChatGPT Prompt, to help marketers elevate their strategies and stay competitive in the digital landscape. The platform also provides the latest AI news, use cases, and insights on how AI can revolutionize marketing practices. With a focus on innovation and cutting-edge technology, TopMarketingAI.com aims to empower marketers with the tools and knowledge needed to succeed in the ever-evolving marketing industry.

This repository includes papers and blogs about Efficient Transformers, Length Extrapolation, Long Term Memory, Retrieval Augmented Generation(RAG), and Evaluation for Long Context Modeling.

Awesome-Segment-Anything is a powerful tool for segmenting and extracting information from various types of data. It provides a user-friendly interface to easily define segmentation rules and apply them to text, images, and other data formats. The tool supports both supervised and unsupervised segmentation methods, allowing users to customize the segmentation process based on their specific needs. With its versatile functionality and intuitive design, Awesome-Segment-Anything is ideal for data analysts, researchers, content creators, and anyone looking to efficiently extract valuable insights from complex datasets.

AI.Hub is a website displaying cards with artificial intelligences (AIs) for various applications, developed using HTML, CSS, and JavaScript. The site aims to democratize access to disruptive AIs by categorizing and organizing them dynamically. It features a responsive layout, search bar for filtering AIs, interactive cards, and column expansion on mobile devices. The project promotes innovation and helps users explore the potential of new technologies.

ProbSem is a repository that provides a framework to leverage large language models (LLMs) for assigning context-conditional probability distributions over queried strings. It supports OpenAI engines and HuggingFace CausalLM models, and is flexible for research applications in linguistics, cognitive science, program synthesis, and NLP. Users can define prompts, contexts, and queries to derive probability distributions over possible completions, enabling tasks like cloze completion, multiple-choice QA, semantic parsing, and code completion. The repository offers CLI and API interfaces for evaluation, with options to customize models, normalize scores, and adjust temperature for probability distributions.

LearnOpenCV is a repository containing code for Computer Vision, Deep learning, and AI research articles shared on the blog LearnOpenCV.com. It serves as a resource for individuals looking to enhance their expertise in AI through various courses offered by OpenCV. The repository includes a wide range of topics such as image inpainting, instance segmentation, robotics, deep learning models, and more, providing practical implementations and code examples for readers to explore and learn from.

cellseg-models.pytorch is a Python library built upon PyTorch for 2D cell/nuclei instance segmentation models. It provides multi-task encoder-decoder architectures and post-processing methods for segmenting cell/nuclei instances. The library offers high-level API to define segmentation models, open-source datasets for training, flexibility to modify model components, sliding window inference, multi-GPU inference, benchmarking utilities, regularization techniques, and example notebooks for training and finetuning models with different backbones.

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