Best AI tools for< Visualize Network >

ResearchRabbit is a research tool that helps researchers discover and organize academic papers. It uses artificial intelligence to recommend papers that are relevant to a researcher's interests and to visualize networks of papers and co-authorships. ResearchRabbit also allows researchers to collaborate on collections of papers and to share their findings with others.

LatenceTech is a tech startup that specializes in network latency monitoring and analysis. The platform offers real-time monitoring, prediction, and in-depth analysis of network latency using AI software. It provides cloud-based network analytics, versatile network applications, and data science-driven network acceleration. LatenceTech focuses on customer satisfaction by providing full customer experience service and expert support. The platform helps businesses optimize network performance, minimize latency issues, and achieve faster network speed and better connectivity.

The website offers interactive tutorials on neural networks and deep learning, providing a comprehensive platform for mastering neural networks in an intuitive, natural, and cohesive manner. Users can access a visualized neural network lab with simplified datasets, a variety of 2D and 3D datasets for regression and classification, and interactive missions to deepen understanding. The platform also features intuitive tutorials, well-visualized neural network knowledge with charts and animations, and a visual deep learning model editor for efficient model building. Overall, it aims to enhance learning and understanding of neural networks through interactive and visual tools.

Inkdrop is an AI-powered tool that helps users visualize their cloud infrastructure by automatically generating interactive diagrams of cloud resources and dependencies. It provides a comprehensive overview of the infrastructure to speed up onboarding and understand complex resource relationships for effective troubleshooting. With seamless integration, users can effortlessly update documentation via CI pipeline integration. Meet the founders Antoine Descamps, Cofounder and CEO, and Alberto Schillaci, Cofounder and CTO. Inkdrop is trusted by partners who believe in its mission.

DealGraph is an AI-powered platform that helps users explore business networks by leveraging hidden relationships among organizations. By analyzing unstructured texts and images, DealGraph provides Relationship Intelligence, allowing users to gain instant knowledge of connections their prospects, customers, or competitors have with other organizations. The platform offers Relationship Alerts to notify users of new relationships, enabling them to uncover crucial insights and opportunities without the need for extensive research. DealGraph also features a Relationship Graph that visualizes organization networks through interactive graphs, answering key questions about market dynamics, customer relationships, industry trends, and supply chain structures. It caters to various users, including sales teams for prospecting, investors for company research, and corporate development teams for market overviews.

This Beach Does Not Exist is an AI application powered by StyleGAN2-ADA network, capable of generating realistic beach images. The website showcases AI-generated beach landscapes created from a dataset of approximately 20,000 images. Users can explore the training progress of the network, generate random images, utilize K-Means Clustering for image grouping, and download the network for experimentation or retraining purposes. Detailed technical information about the network architecture, dataset, training steps, and metrics is provided. The application is based on the GAN architecture developed by NVIDIA Labs and offers a unique experience of creating virtual beach scenes through AI technology.

Structurepedia is an AI-powered platform that maps the structure of knowledge by providing structured and interactive information on various topics, including neural network architecture variants and other important concepts in machine learning and artificial intelligence. It offers a new way to learn by allowing users to explore topics through visual diagrams and detailed resources, making it easier to understand complex information. Structurepedia aims to revolutionize the way people access and comprehend knowledge in the age of AI, acting as a modern encyclopedia and search engine tailored for the AI era.

LogRocket is a session replay, product analytics, and issue detection platform that helps software teams deliver the best web and mobile experiences. With LogRocket, you can see exactly what users experienced on your app, as well as DOM playback, console and network logs, errors, and performance data. You can also surface the most impactful user issues with JavaScript errors, network errors, stack traces, automatic triaging, and alerting. LogRocket also provides product analytics to help you understand how users are interacting with your app, and UX analytics to help you visualize how users experience your app at both the individual and aggregate level.

Cloudinary is a cloud-based platform that provides image and video management, optimization, and delivery services. It offers a range of features including image and video storage, transformation, optimization, and delivery, as well as AI-powered features such as generative AI, machine learning, and content-aware AI. Cloudinary's platform is designed to help businesses improve the performance, engagement, and efficiency of their visual content.

Uizard is an AI-powered UI design tool that simplifies the process of creating user interfaces, wireframes, mockups, and prototypes. It offers a range of features that enable users to generate designs from text prompts or screenshots, create themes, and transform hand-drawn sketches into digital designs. Uizard empowers product teams to visualize, communicate, and iterate on design concepts quickly and efficiently, making it an essential tool for designers, product managers, marketers, and developers.

BabyFaceGenerator is an AI-based tool that analyzes up to 70 facial features of two partners to generate the face of a future baby. While it provides entertainment by predicting what a baby might look like, it is important to note that genetics are much more complex than the tool can accurately represent. The tool is available in multiple languages and offers a fun way for users to visualize potential offspring.

Spacely AI is an AI rendering solution that specializes in interior, room, and home design. It utilizes advanced artificial intelligence algorithms to create realistic and immersive visualizations of interior spaces. With Spacely AI, users can easily experiment with different design elements, layouts, and color schemes to bring their ideas to life. Whether you are an interior designer, architect, or homeowner, Spacely AI provides a powerful tool to visualize and plan your design projects with precision and creativity.

Getfloorplan is an AI-powered platform that allows users to create 2D and 3D floor plans, as well as virtual tours for real estate properties. The application offers various sets of property visuals at different price points, starting from basic 2D plans to high-quality renderings. Users can upload a floor plan and receive realistic and attractive visuals within 24 hours, without the need for human involvement. Getfloorplan guarantees the lowest price and offers a money-back guarantee if users are unsatisfied with the results.

SeeYourBabyAI is an AI-powered platform that offers a unique service to predict the appearance of your future baby based on photos of you and your partner. By leveraging advanced AI technology, the platform generates realistic images of potential sons and daughters with a high level of accuracy. Users can upload their photos, receive multiple AI-generated baby photos, and share them with family and friends. The platform focuses on precision, respects ethnic backgrounds, ensures privacy through encryption, and provides high-resolution, realistic photos for a one-time payment. SeeYourBabyAI aims to provide users with a heartwarming and fun experience of visualizing their future children.

VisualizeAI is a powerful AI-powered platform that helps businesses visualize and analyze their data. With VisualizeAI, you can easily create stunning data visualizations, dashboards, and reports that will help you make better decisions. VisualizeAI is perfect for businesses of all sizes, from startups to large enterprises. It is easy to use and affordable, and it can help you save time and money while improving your decision-making.

Quick Dreamviz is an instant dream home visualization tool that allows users to redesign their rooms using AI technology. With just a few clicks, users can upload a photo of their room, select a room type and theme, and watch as the AI generates a new design. Quick Dreamviz is perfect for anyone who wants to see how their dream home will look before it becomes a reality.

Visuali is an AI-powered generative art tool that allows users to turn their imagination into reality. With Visuali, users can create stunning images and videos from scratch, or they can use Visuali's pre-trained models to generate unique and inspiring content. Visuali is perfect for artists, designers, and anyone who wants to explore the possibilities of AI-generated art.

FutureKid.ai is an AI-powered application that allows users to generate pictures of their future kids using just one image of each parent. The application uses advanced AI algorithms to create realistic HD images that provide a glimpse into the user's future family. The process is automated, ensuring user privacy by deleting all data from servers within 24 hours. FutureKid.ai is a German-based company that values user privacy and offers a unique way to visualize potential future offspring.

Bristles AI is a powerful AI tool designed for DIY enthusiasts, furniture designers, and home renovators. It allows users to create design mock-ups for furniture flips and home updates, helping them visualize and customize their projects before diving into the actual work. With features like generating design ideas, customizing designs, and sharing clear mock-ups, Bristles AI empowers users to bring their creative visions to life with confidence. The application has garnered praise from furniture artists, contractors, and DIYers for its user-friendly interface and ability to streamline the design process.

AI VisionBoard Launch App is an AI-powered application that allows users to create personalized vision boards to visualize their dreams and aspirations. Users can quickly visualize their dreams in seconds by typing them out or using random prompt ideas. The app also enables users to add their photos and see themselves in their dreams. Additionally, users can explore a community of shared dreams, share their vision board creations, and connect with like-minded individuals. The app also features an AI Life Coach chat function for personal growth and well-being support, providing users with a 24/7 companion. AI VisionBoard aims to help users turn their aspirations into reality through visualization and community support.

cl-waffe2 is an experimental deep learning framework in Common Lisp, providing fast, systematic, and customizable matrix operations, reverse mode tape-based Automatic Differentiation, and neural network model building and training features accelerated by a JIT Compiler. It offers abstraction layers, extensibility, inlining, graph-level optimization, visualization, debugging, systematic nodes, and symbolic differentiation. Users can easily write extensions and optimize their networks without overheads. The framework is designed to eliminate barriers between users and developers, allowing for easy customization and extension.

LLM-Viewer is a tool for visualizing Language and Learning Models (LLMs) and analyzing performance on different hardware platforms. It enables network-wise analysis, considering factors such as peak memory consumption and total inference time cost. With LLM-Viewer, users can gain valuable insights into LLM inference and performance optimization. The tool can be used in a web browser or as a command line interface (CLI) for easy configuration and visualization. The ongoing project aims to enhance features like showing tensor shapes, expanding hardware platform compatibility, and supporting more LLMs with manual model graph configuration.

The LLM course is divided into three parts: 1. 🧩 **LLM Fundamentals** covers essential knowledge about mathematics, Python, and neural networks. 2. 🧑‍🔬 **The LLM Scientist** focuses on building the best possible LLMs using the latest techniques. 3. 👷 **The LLM Engineer** focuses on creating LLM-based applications and deploying them. For an interactive version of this course, I created two **LLM assistants** that will answer questions and test your knowledge in a personalized way: * 🤗 **HuggingChat Assistant**: Free version using Mixtral-8x7B. * 🤖 **ChatGPT Assistant**: Requires a premium account. ## 📝 Notebooks A list of notebooks and articles related to large language models. ### Tools | Notebook | Description | Notebook | |----------|-------------|----------| | 🧐 LLM AutoEval | Automatically evaluate your LLMs using RunPod |  | | 🥱 LazyMergekit | Easily merge models using MergeKit in one click. |  | | 🦎 LazyAxolotl | Fine-tune models in the cloud using Axolotl in one click. |  | | ⚡ AutoQuant | Quantize LLMs in GGUF, GPTQ, EXL2, AWQ, and HQQ formats in one click. |  | | 🌳 Model Family Tree | Visualize the family tree of merged models. |  | | 🚀 ZeroSpace | Automatically create a Gradio chat interface using a free ZeroGPU. |  |

PINNACLE is a flexible geometric deep learning approach that trains on contextualized protein interaction networks to generate context-aware protein representations. It provides protein representations split across various cell-type contexts from different tissues and organs. The tool can be fine-tuned to study the genomic effects of drugs and nominate promising protein targets and cell-type contexts for further investigation. PINNACLE exemplifies the paradigm of incorporating context-specific effects for studying biological systems, especially the impact of disease and therapeutics.

This repository provides advanced AI explainability for PyTorch, offering state-of-the-art methods for Explainable AI in computer vision. It includes a comprehensive collection of Pixel Attribution methods for various tasks like Classification, Object Detection, Semantic Segmentation, and more. The package supports high performance with full batch image support and includes metrics for evaluating and tuning explanations. Users can visualize and interpret model predictions, making it suitable for both production and model development scenarios.

A web application to explore the capabilities of Google Maps Platform Route Optimization (GMPRO). It helps users understand the data model and functions of the API by presenting interactive forms, tables, and maps. The tool is intended for exploratory use only and should not be deployed in production. Users can construct scenarios, tune constraint parameters, and visualize routes before implementing their own solutions for integrating Route Optimization into their business processes. The application incurs charges related to cloud resources and API usage, and users should be cautious about generating high usage volumes, especially for large scenarios.

AITemplate (AIT) is a Python framework that transforms deep neural networks into CUDA (NVIDIA GPU) / HIP (AMD GPU) C++ code for lightning-fast inference serving. It offers high performance close to roofline fp16 TensorCore (NVIDIA GPU) / MatrixCore (AMD GPU) performance on major models. AITemplate is unified, open, and flexible, supporting a comprehensive range of fusions for both GPU platforms. It provides excellent backward capability, horizontal fusion, vertical fusion, memory fusion, and works with or without PyTorch. FX2AIT is a tool that converts PyTorch models into AIT for fast inference serving, offering easy conversion and expanded support for models with unsupported operators.

Weights & Biases (W&B) is a platform that helps users build better machine learning models faster by tracking and visualizing all components of the machine learning pipeline, from datasets to production models. It offers tools for tracking, debugging, evaluating, and monitoring machine learning applications. W&B provides integrations with popular frameworks like PyTorch, TensorFlow/Keras, Hugging Face Transformers, PyTorch Lightning, XGBoost, and Sci-Kit Learn. Users can easily log metrics, visualize performance, and compare experiments using W&B. The platform also supports hosting options in the cloud or on private infrastructure, making it versatile for various deployment needs.

GOLEM is an open-source AI framework focused on optimization and learning of structured graph-based models using meta-heuristic methods. It emphasizes the potential of meta-heuristics in complex problem spaces where gradient-based methods are not suitable, and the importance of structured models in various problem domains. The framework offers features like structured model optimization, metaheuristic methods, multi-objective optimization, constrained optimization, extensibility, interpretability, and reproducibility. It can be applied to optimization problems represented as directed graphs with defined fitness functions. GOLEM has applications in areas like AutoML, Bayesian network structure search, differential equation discovery, geometric design, and neural architecture search. The project structure includes packages for core functionalities, adapters, graph representation, optimizers, genetic algorithms, utilities, serialization, visualization, examples, and testing. Contributions are welcome, and the project is supported by ITMO University's Research Center Strong Artificial Intelligence in Industry.

A web application to explore the capabilities of Google Maps Platform Route Optimization (GMPRO) for solving vehicle routing problems. Users can interact with the GMPRO data model through forms, tables, and maps to construct scenarios, tune constraints, and visualize routes. The application is intended for exploration purposes only and should not be deployed in production. Users are responsible for billing related to cloud resources and API usage. It is important to understand the pricing models for Maps Platform and Route Optimization before using the application.

This repository contains a collection of AI projects covering various areas of machine learning. Each project is accompanied by detailed articles on the associated blog sciblog. Projects range from introductory topics like Convolutional Neural Networks and Transfer Learning to advanced topics like Fraud Detection and Recommendation Systems. The repository also includes tutorials on data generation, distributed training, natural language processing, and time series forecasting. Additionally, it features visualization projects such as football match visualization using Datashader.

ByteChef is an open-source, low-code, extendable API integration and workflow automation platform. It provides an intuitive UI Workflow Editor, event-driven & scheduled workflows, multiple flow controls, built-in code editor supporting Java, JavaScript, Python, and Ruby, rich component ecosystem, extendable with custom connectors, AI-ready with built-in AI components, developer-ready to expose workflows as APIs, version control friendly, self-hosted, scalable, and resilient. It allows users to build and visualize workflows, automate tasks across SaaS apps, internal APIs, and databases, and handle millions of workflows with high availability and fault tolerance.

imodelsX is a Scikit-learn friendly library that provides tools for explaining, predicting, and steering text models/data. It also includes a collection of utilities for getting started with text data. **Explainable modeling/steering** | Model | Reference | Output | Description | |---|---|---|---| | Tree-Prompt | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/tree_prompt) | Explanation + Steering | Generates a tree of prompts to steer an LLM (_Official_) | | iPrompt | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/iprompt) | Explanation + Steering | Generates a prompt that explains patterns in data (_Official_) | | AutoPrompt | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/autoprompt) | Explanation + Steering | Find a natural-language prompt using input-gradients (⌛ In progress)| | D3 | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/d3) | Explanation | Explain the difference between two distributions | | SASC | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/sasc) | Explanation | Explain a black-box text module using an LLM (_Official_) | | Aug-Linear | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/aug_linear) | Linear model | Fit better linear model using an LLM to extract embeddings (_Official_) | | Aug-Tree | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/aug_tree) | Decision tree | Fit better decision tree using an LLM to expand features (_Official_) | **General utilities** | Model | Reference | |---|---| | LLM wrapper| [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/llm) | Easily call different LLMs | | | Dataset wrapper| [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/data) | Download minimially processed huggingface datasets | | | Bag of Ngrams | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/bag_of_ngrams) | Learn a linear model of ngrams | | | Linear Finetune | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/linear_finetune) | Finetune a single linear layer on top of LLM embeddings | | **Related work** * [imodels package](https://github.com/microsoft/interpretml/tree/main/imodels) (JOSS 2021) - interpretable ML package for concise, transparent, and accurate predictive modeling (sklearn-compatible). * [Adaptive wavelet distillation](https://arxiv.org/abs/2111.06185) (NeurIPS 2021) - distilling a neural network into a concise wavelet model * [Transformation importance](https://arxiv.org/abs/1912.04938) (ICLR 2020 workshop) - using simple reparameterizations, allows for calculating disentangled importances to transformations of the input (e.g. assigning importances to different frequencies) * [Hierarchical interpretations](https://arxiv.org/abs/1807.03343) (ICLR 2019) - extends CD to CNNs / arbitrary DNNs, and aggregates explanations into a hierarchy * [Interpretation regularization](https://arxiv.org/abs/2006.14340) (ICML 2020) - penalizes CD / ACD scores during training to make models generalize better * [PDR interpretability framework](https://www.pnas.org/doi/10.1073/pnas.1814225116) (PNAS 2019) - an overarching framewwork for guiding and framing interpretable machine learning

The Microsoft Health Intelligence Machine Learning Toolbox is a repository that provides low-level and high-level building blocks for Machine Learning / AI researchers and practitioners. It simplifies and streamlines work on deep learning models for healthcare and life sciences by offering tested components such as data loaders, pre-processing tools, deep learning models, and cloud integration utilities. The repository includes two Python packages, 'hi-ml-azure' for helper functions in AzureML, 'hi-ml' for ML components, and 'hi-ml-cpath' for models and workflows related to histopathology images.

Kafka-ML is a framework designed to manage the pipeline of Tensorflow/Keras and PyTorch machine learning models on Kubernetes. It enables the design, training, and inference of ML models with datasets fed through Apache Kafka, connecting them directly to data streams like those from IoT devices. The Web UI allows easy definition of ML models without external libraries, catering to both experts and non-experts in ML/AI.

Databend is an open-source cloud data warehouse that serves as a cost-effective alternative to Snowflake. With its focus on fast query execution and data ingestion, it's designed for complex analysis of the world's largest datasets.

Cognee is an open-source framework designed for creating self-improving deterministic outputs for Large Language Models (LLMs) using graphs, LLMs, and vector retrieval. It provides a platform for AI engineers to enhance their models and generate more accurate results. Users can leverage Cognee to add new information, utilize LLMs for knowledge creation, and query the system for relevant knowledge. The tool supports various LLM providers and offers flexibility in adding different data types, such as text files or directories. Cognee aims to streamline the process of working with LLMs and improving AI models for better performance and efficiency.

MINT benchmark aims to evaluate LLMs' ability to solve tasks with multi-turn interactions by (1) using tools and (2) leveraging natural language feedback.

Expert in creating complex network diagrams with customizable styles and shapes.

Generates artistic hacker and cybersecurity themed visuals.

Detailed technical diagram and graph assistant

Analyse & visualise semantic networks entities and attributes for content creation.

I craft visuals for medical concepts.

Visualize and inform on bouquets.

This GPT will help you set and visualize an intention for a spiritual ceremony

Visualize & learn about history

I'm an interior decorator using DALL-E 3 to visualize your space's potential.

Visualize D&D adventures with stunning, lore-accurate art. Huzzah!

A film director helping to visualize scripts into storyboards.

Helps character artists visualize items for characters with photo-realistic images.

Visualize your dreams at night.밤에 꾼 꿈을 시각화 해드립니다..จินตนาการความฝันของคุณในเวลากลางคืน.夜の夢を視覚化する.रात में अपने सपनों को दिखाएं.Hãy hình dung giấc mơ của bạn vào ban đêm.Bayangkan mimpimu di malam hari.在夜間可視化您的夢境

I craft and visualize 'what if' histories.

I craft and visualize 2x2 strategy frameworks.

A guide to visualize and manifest your desires through the Law of Attraction.

International Time Zone Meeting Planner / Converter (independently verify info received). Meet your AI assistant for managing international time zones, specializing in coordinating meetings & events across different regions. Effortlessly plan & visualize physical & digital global engagements.

I guide chess strategy and visualize board states.

Explore & interpret the Eurostat database. Type in requests for statistics, also ask to visualize it. Works best wish specific datasets. It's meant for professionals familiar with the Eurostat database looking for a faster way to explore it.

Master the art of Google Sheets with an assistant who can do everything from answer questions about basic features, explain functions in an eloquent and succinct manner, simplify the most complex formulas into easy steps, and help you identify techniques to effectively visualize your data.