Best AI tools for< Visualize Training Progress >

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.

Blackshark.ai is an AI-based platform that generates real-time accurate semantic photorealistic 3D digital twin of the entire planet. The platform extracts insights about the planet's infrastructure from satellite and aerial imagery using machine learning at a global scale. It provides a photorealistic, geo-typical, or asset-specific digital twin for visualization, simulation, mapping, mixed reality environments, and other enterprise solutions. The platform offers features such as 3D Semantic Map, Synthetic Environments, ORCA™HUNTR for object identification, SYNTH3D for 3D replica of the planet's surface, Digital Airports, Synthetic Training Data, Semantic 3D City Models, and Geospatial Analytics.

SkyHive is an AI-powered platform that aims to organize the world's workforce data and facilitate reskilling. It offers solutions for businesses, governments, educators, and individuals to leverage skills-based models, automate processes, and drive employee engagement. SkyHive's technology utilizes advanced skills matching algorithms, real-time labor market data, and predictive analytics to help users identify skill gaps, connect to opportunities, and accelerate career advancement.

Comet ML is an extensible, fully customizable machine learning platform that aims to move ML forward by supporting productivity, reproducibility, and collaboration. It integrates with existing infrastructure and tools to manage, visualize, and optimize models from training runs to production monitoring. Users can track and compare training runs, create a model registry, and monitor models in production all in one platform. Comet's platform can be run on any infrastructure, enabling users to reshape their ML workflow and bring their existing software and data stack.

DecorAI.xyz is an AI-driven interior design tool that allows users to generate dream rooms using artificial intelligence. By simply taking a picture of a room, users can see how it looks in different themes and receive personalized design suggestions. With exceptional training on a massive dataset of 160 million design samples, DecorAI optimizes spatial layouts, provides cost-effective design solutions, and saves time compared to traditional interior design methods. The tool caters to homeowners, renters, and small businesses looking to redesign their spaces without the need for an expensive interior designer.

Data Science Dojo is a globally recognized e-learning platform that offers programs in data science, data analytics, machine learning, and more. They provide comprehensive and hands-on training in various formats such as in-person, virtual instructor-led, and self-paced training. The focus is on helping students develop a think-business-first mindset to apply their data science skills effectively in real-world scenarios. With over 2500 enterprises trained, Data Science Dojo aims to make data science accessible to everyone.

DVC Studio is a collaboration tool for machine learning teams. It provides seamless data and model management, experiment tracking, visualization, and automation. DVC Studio is built for ML researchers, practitioners, and managers. It enables model organization and discovery across all ML projects and manages model lifecycle with Git, unifying ML projects with the best DevOps practices. DVC Studio also provides ML experiment tracking, visualization, collaboration, and automation using Git. It applies software engineering and DevOps best-practices to automate ML bookkeeping and model training, enabling easy collaboration and faster iterations.

Weights & Biases is a machine learning platform that helps data scientists and engineers build, train, and deploy machine learning models. It provides a central location to track and manage all of your machine learning projects, and it offers a variety of tools to help you collaborate with others and share your work.

Comet ML is a machine learning platform that integrates with your existing infrastructure and tools so you can manage, visualize, and optimize models—from training runs to production monitoring.

Comet ML is a machine learning platform that integrates with your existing infrastructure and tools so you can manage, visualize, and optimize models—from training runs to production monitoring.

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.

PyTorch Lightning is a framework for training and deploying AI models. It provides a high-level API that abstracts away the low-level details of PyTorch, making it easier to write and maintain complex models. Lightning also includes a number of features that make it easy to train and deploy models on multiple GPUs or TPUs, and to track and visualize training progress. PyTorch Lightning is used by a wide range of organizations, including Google, Facebook, and Microsoft. It is also used by researchers at top universities around the world. Here are some of the benefits of using PyTorch Lightning: * **Increased productivity:** Lightning's high-level API makes it easy to write and maintain complex models. This can save you time and effort, and allow you to focus on the research or business problem you're trying to solve. * **Improved performance:** Lightning's optimized training loops and data loading pipelines can help you train models faster and with better performance. * **Easier deployment:** Lightning makes it easy to deploy models to a variety of platforms, including the cloud, on-premises servers, and mobile devices. * **Better reproducibility:** Lightning's logging and visualization tools make it easy to track and reproduce training results.

Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals.

Aim is an open-source, self-hosted ML experiment tracking tool designed to handle 10,000s of training runs. Aim provides a performant and beautiful UI for exploring and comparing training runs. Additionally, its SDK enables programmatic access to tracked metadata — perfect for automations and Jupyter Notebook analysis. **Aim's mission is to democratize AI dev tools 🎯**

Awesome MLOps is a curated list of tools related to Machine Learning Operations, covering areas such as AutoML, CI/CD for Machine Learning, Data Cataloging, Data Enrichment, Data Exploration, Data Management, Data Processing, Data Validation, Data Visualization, Drift Detection, Feature Engineering, Feature Store, Hyperparameter Tuning, Knowledge Sharing, Machine Learning Platforms, Model Fairness and Privacy, Model Interpretability, Model Lifecycle, Model Serving, Model Testing & Validation, Optimization Tools, Simplification Tools, Visual Analysis and Debugging, and Workflow Tools. The repository provides a comprehensive collection of tools and resources for individuals and teams working in the field of MLOps.

SwanLab is an open-source, lightweight AI experiment tracking tool that provides a platform for tracking, comparing, and collaborating on experiments, aiming to accelerate the research and development efficiency of AI teams by 100 times. It offers a friendly API and a beautiful interface, combining hyperparameter tracking, metric recording, online collaboration, experiment link sharing, real-time message notifications, and more. With SwanLab, researchers can document their training experiences, seamlessly communicate and collaborate with collaborators, and machine learning engineers can develop models for production faster.

This repository evaluates Large Language Models (LLMs) trained on PGN format chess games using linear probes. It assesses the LLMs' internal understanding of board state and their ability to estimate player skill levels. The repo provides tools to train, evaluate, and visualize linear probes on LLMs trained to play chess with PGN strings. Users can visualize the model's predictions, perform interventions on the model's internal board state, and analyze board state and player skill level accuracy across different LLMs. The experiments in the repo can be conducted with less than 1 GB of VRAM, and training probes on the 8 layer model takes about 10 minutes on an RTX 3050. The repo also includes scripts for performing board state interventions and skill interventions, along with useful links to open-source code, models, datasets, and pretrained models.

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

MATLAB-Simulink-Challenge-Project-Hub is a repository aimed at contributing to the progress of engineering and science by providing challenge projects with real industry relevance and societal impact. The repository offers a wide range of projects covering various technology trends such as Artificial Intelligence, Autonomous Vehicles, Big Data, Computer Vision, and Sustainability. Participants can gain practical skills with MATLAB and Simulink while making a significant contribution to science and engineering. The projects are designed to enhance expertise in areas like Sustainability and Renewable Energy, Control, Modeling and Simulation, Machine Learning, and Robotics. By participating in these projects, individuals can receive official recognition for their problem-solving skills from technology leaders at MathWorks and earn rewards upon project completion.

DL3DV-10K is a large-scale dataset of real-world scene-level videos with annotations, covering diverse scenes with different levels of reflection, transparency, and lighting. It includes 10,510 multi-view scenes with 51.2 million frames at 4k resolution, and offers benchmark videos for novel view synthesis (NVS) methods. The dataset is designed to facilitate research in deep learning-based 3D vision and provides valuable insights for future research in NVS and 3D representation learning.

The Looker GenAI Extension provides code examples and resources for building a Looker Extension that integrates with Vertex AI Large Language Models (LLMs). Users can leverage the power of LLMs to enhance data exploration and analysis within Looker. The extension offers generative explore functionality to ask natural language questions about data and generative insights on dashboards to analyze data by asking questions. It leverages components like BQML Remote Models, BQML Remote UDF with Vertex AI, and Custom Fine Tune Model for different integration options. Deployment involves setting up infrastructure with Terraform and deploying the Looker Extension by creating a Looker project, copying extension files, configuring BigQuery connection, connecting to Git, and testing the extension. Users can save example prompts and configure user settings for the extension. Development of the Looker Extension environment includes installing dependencies, starting the development server, and building for production.

Pearl is a production-ready Reinforcement Learning AI agent library open-sourced by the Applied Reinforcement Learning team at Meta. It enables researchers and practitioners to develop Reinforcement Learning AI agents that prioritize cumulative long-term feedback over immediate feedback and can adapt to environments with limited observability, sparse feedback, and high stochasticity. Pearl offers a diverse set of unique features for production environments, including dynamic action spaces, offline learning, intelligent neural exploration, safe decision making, history summarization, and data augmentation.

A curated list of Generative AI projects, tools, artworks, and models

AiTreasureBox is a versatile AI tool that provides a collection of pre-trained models and algorithms for various machine learning tasks. It simplifies the process of implementing AI solutions by offering ready-to-use components that can be easily integrated into projects. With AiTreasureBox, users can quickly prototype and deploy AI applications without the need for extensive knowledge in machine learning or deep learning. The tool covers a wide range of tasks such as image classification, text generation, sentiment analysis, object detection, and more. It is designed to be user-friendly and accessible to both beginners and experienced developers, making AI development more efficient and accessible to a wider audience.

Flyte is an open-source orchestrator that facilitates building production-grade data and ML pipelines. It is built for scalability and reproducibility, leveraging Kubernetes as its underlying platform. With Flyte, user teams can construct pipelines using the Python SDK, and seamlessly deploy them on both cloud and on-premises environments, enabling distributed processing and efficient resource utilization.

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.

InsightArt is your compassionate art therapy guide, leveraging the power of DALL-E 3 to create and interpret artworks that mirror your emotional journey. It's here to help you visualize and explore your feelings, offering a unique blend of artistic creativity and therapeutic insight.

This is a brief generator, it will help generate ideas for your new art. It will follow your prompts and in the end will provide a description of the art, visualize it, provide a color palette and RGB code to it. Also, make a ready to use prompt for MidJourney. Have fun!

Charming coffee visualiser

Expert in visualizing industrial designs via DALL-E