Search Results for "model-builder" - Page 79
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Showing 2225 open source projects for "model-builder"
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Stop Cyber Threats with VM-Series Next-Gen Firewall on AzureGain integrated visibility across all traffic in a single pass. Deploy Palo Alto Networks VM-Series to determine application identity and content while automating security policy updates via rich APIs.
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Enterprise-grade ITSM, for every businessFreshservice is an intuitive, AI-powered platform that helps IT, operations, and business teams deliver exceptional service without the usual complexity. Automate repetitive tasks, resolve issues faster, and provide seamless support across the organization. From managing incidents and assets to driving smarter decisions, Freshservice makes it easy to stay efficient and scale with confidence.
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NASH OS
Nash Operating System for Modern Ecommerce
The all-built-in-one, automatic, ready-to-go out-of-box, easy-to-use state-of-the-art, and really awesome NASH OS! Over 25,000+ flexible features and controls and all scalable!! The most powerful solution ever built to instantly deliver new heights of online ecommerce enterprise to you. -
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The MCAPL Project provides a tool for prototyping BDI agent programming languages and model checking programs written in these languages via an interface to the JavaPathfinder model checker.
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BossSensor
Hide screen when boss is approaching
...Training the system requires a dataset of labeled images representing the boss and other people so that the model can learn to differentiate between them. -
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Finetune Transformer LM
Code for "Improving Language Understanding by Generative Pre-Training"
finetune-transformer-lm is a research codebase that accompanies the paper “Improving Language Understanding by Generative Pre-Training,” providing a minimal implementation focused on fine-tuning a transformer language model for evaluation tasks. The repository centers on reproducing the ROCStories Cloze Test result and includes a single-command training workflow to run the experiment end to end. It documents that runs are non-deterministic due to certain GPU operations and reports a median accuracy over multiple trials that is slightly below the single-run result in the paper, reflecting expected variance in practice. ... -
Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
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Improved GAN
Code for the paper "Improved Techniques for Training GANs"
...By offering structured experiments across multiple datasets, it allows researchers to study and replicate the improvements described in the paper. Although the project is archived and not actively maintained, it remains a reference point in the history of GAN research, influencing subsequent model training approaches. -
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Dynamic Routing Between Capsules
A PyTorch implementation of the NIPS 2017 paper
...The repository implements the dynamic routing algorithm between capsules, which allows lower-level features to route their outputs to higher-level structures that best represent the detected patterns. This approach enables the model to capture part-to-whole relationships in visual data more effectively than standard CNNs. The project serves primarily as a research implementation that demonstrates how capsule networks can be built and trained using modern deep learning frameworks. -
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Market Reporter
Automatic Generation of Brief Summaries of Time-Series Data
...Suppose you have a database named master on your local machine. Prediction submodule generates a single comment of a financial instrument at specified time by loading a trained model. -
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EECluster
Tool for energy-efficient resource management in HPC clusters
...EECluster uses a Hybrid Genetic Fuzzy System as the decision-making mechanism that elicits part of its rule base dependent on the cluster workload scenario, delivering good compliance with the administrator preferences. In the latest version, we leverage a more sophisticated and exhaustive model that covers a wider range of environmental aspects and balances service quality and power consumption with all indirect costs, including hardware failures and subsequent replacements, measured in both monetary units and carbon emissions. This software has been developed by Quantum and High Performance Computing group (QHPC). See https://qhpc.uniovi.es/ -
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Machine learning Resources
Some learning materials and research introduction on machine learning
...It serves as a curated knowledge base that introduces fundamental algorithms and techniques used in modern machine learning systems. The repository organizes materials that cover topics such as classification algorithms, neural networks, feature engineering, and model evaluation. Many sections reference research papers, tutorials, and open-source implementations that allow users to explore specific machine learning methods in greater depth. The project is maintained by researcher Jindong Wang, whose work focuses on machine learning research areas including transfer learning, domain adaptation, and robust learning methods. -
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Delphi Face Detection
Delphi face detection
...Runs on Windows XP to Windows 10 Available on MsAcceSS or SQLite Dataface! Depending on Request! You can reprogram / setup / configure / scale the Face Recognition accuracy! C++Builder Face Detection: FREE Source code! https://sourceforge.net/projects/cbuilder-opencv-face-detection/ C++Builder Face Recognition: https://sourceforge.net/projects/c-builder-face-recognition/ Technical Support is always online, thank you -
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LUMINOTH
Deep Learning toolkit for Computer Vision
LUMINOTH is an open-source deep learning toolkit designed for computer vision tasks, particularly object detection. The framework is implemented in Python and built on top of TensorFlow and the Sonnet neural network library, providing a modular environment for training and deploying detection models. It was created to simplify the process of building and experimenting with deep learning models capable of identifying objects within images. Luminoth includes support for popular object... -
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Skater
Python library for model interpretation/explanations
Skater is a unified framework to enable Model Interpretation for all forms of the model to help one build an Interpretable machine learning system often needed for real-world use-cases(** we are actively working towards to enabling faithful interpretability for all forms models). It is an open-source python library designed to demystify the learned structures of a black box model both globally(inference on the basis of a complete data set) and locally(inference about an individual prediction). ... -
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Deepvoice3_pytorch
PyTorch implementation of convolutional neural networks
An open source implementation of Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning. -
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Learn_Data_Science_in_3_Months
This is the Curriculum for "Learn Data Science in 3 Months"
...It breaks the journey into clear stages: Python fundamentals, data wrangling, visualization, statistics, machine learning, and end-to-end projects. The schedule mixes learning and doing, encouraging you to build small deliverables each week—like notebooks, dashboards, and model demos—to reinforce skills. It also includes suggestions for datasets and problem domains so you aren’t stuck wondering what to analyze next. The plan is intentionally opinionated but flexible: you can swap resources while keeping the weekly objectives intact. By the end, you’re expected to have tangible artifacts to show employers or collaborators, not just notes and bookmarks. -
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SG2Im
Code for "Image Generation from Scene Graphs", Johnson et al, CVPR 201
...The pipeline typically predicts object layouts (bounding boxes and masks) from the graph, then renders a realistic image conditioned on those layouts. This separation lets the model reason about geometry and composition before committing to texture and color, improving spatial fidelity. The repository includes training code, datasets, and evaluation scripts so researchers can reproduce baselines and extend components such as the graph encoder or image generator. In practice, sg2im demonstrates how structured semantics can guide generative models to produce controllable, compositional imagery. -
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Bottery
A conversational agent prototyping platform
Bottery is a prototyping environment and mini-language for designing conversational agents as explicit state machines rather than opaque bundles of code. It encourages authors to model intents, dialog turns, and transitions in a compact, declarative form that’s easy to visualize and simulate. A built-in simulator lets you “play” the conversation, test edge cases, and tweak flows without wiring up back-end services first. Because behavior is defined in a DSL, teams can version the conversation design alongside code and keep product, research, and engineering aligned. ... -
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The GAN Zoo
A list of all named GANs
...The list includes references to many GAN variants along with links to their original research papers and sometimes implementation code. Users can browse the dataset or explore a tabular version that allows filtering by year or searching for specific model names. The repository encourages contributions from the community so that newly published GAN architectures can be added to the list. -
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OpenSeq2Seq
Toolkit for efficient experimentation with Speech Recognition
...It supports multi-GPU and multi-node data-parallel training, and integrates with Horovod to scale out across large GPU clusters. Mixed-precision support (float16) is optimized for NVIDIA Volta and Turing GPUs, allowing significant speedups and memory savings without sacrificing model quality. The project comes with configuration-driven training scripts, documentation, and examples that demonstrate how to set up pipelines for tasks. -
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Pragmatic AI
[Book-2019] Pragmatic AI: An Introduction to Cloud-based ML
Pragmatic AI is the first truly practical guide to solving real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Writing for business professionals, decision-makers, and students who aren’t professional data scientists, Noah Gift demystifies all the tools and technologies you need to get results. He illuminates powerful off-the-shelf cloud-based solutions from Google, Amazon, and Microsoft, as well as accessible techniques using Python... -
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Exposure
Learning infinite-resolution image processing with GAN and RL
Learning infinite-resolution image processing with GAN and RL from unpaired image datasets, using a differentiable photo editing model. ACM Transactions on Graphics (presented at SIGGRAPH 2018) Exposure is originally designed for RAW photos, which assumes 12+ bit color depth and linear "RGB" color space (or whatever we get after demosaicing). jpg and png images typically have only 8-bit color depth (except 16-bit pngs) and the lack of information (dynamic range/activation resolution) may lead to suboptimal results such as posterization. ... -
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CRP - Chemical Reaction Prediction
Predicting Organic Reactions using Neural Networks.
...This Graphical User Interface takes simplified molecular-input line-entry system (SMILES) as an input and generates the product SMILE & molecule. Beam search is used in Version 2, to generate top 5 predictions. Maximum input length for the model is 15 (excluding spaces). -
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Learn_Deep_Learning_in_6_Weeks
This is the Curriculum for "Learn Deep Learning in 6 Weeks"
...The materials emphasize code-first understanding: building small models, training them on accessible datasets, and analyzing their behavior. Each week culminates in a tangible outcome—such as a working classifier or sequence model—so progress is visible and motivating. The plan also introduces practical considerations like GPU usage, checkpoints, and debugging training dynamics. It aims to give you enough breadth to recognize common patterns and enough depth to implement them on your own problems. -
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LearningToCompare_FSL
Learning to Compare: Relation Network for Few-Shot Learning
...The repository provides training and evaluation code for standard few-shot benchmarks such as miniImageNet and Omniglot, making it possible to reproduce the experimental results reported in the paper. It includes model definitions, data loading logic, episodic training loops, and scripts that implement the N-way K-shot evaluation protocol common in few-shot research. Researchers can use this codebase as a starting point to test new ideas, modify relation modules, or transfer the approach to new datasets. -
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Generative Models
Collection of generative models, e.g. GAN, VAE in Pytorch
This project is a comprehensive open-source collection of implementations of various generative machine learning models designed to help researchers and developers experiment with deep generative techniques. The repository contains practical implementations of well-known architectures such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Restricted Boltzmann Machines, and Helmholtz Machines, implemented primarily using modern deep learning frameworks like PyTorch... -
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Presage
the intelligent predictive text entry platform
...Presage computes probabilities for words which are most likely to be entered next by merging predictions generated by the different predictive algorithms. Presage's modular and extensible architecture allows its language model to be extended and customized to utilize statistical, syntactic, and semantic predictive algorithms. Presage's predictive capabilities are implemented by predictive plugins. Predictive plugins use services provided by the platform to implement multiple prediction techniques.
