Search Results for "sandbox:/mnt/data/project_plan.pod" - Page 3
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Showing 110 open source projects for "sandbox:/mnt/data/project_plan.pod"
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MicroStation by Bentley Systems is the trusted computer-aided design (CAD) software built specifically for infrastructure design.MicroStation is the only computer-aided design software for infrastructure design, helping architects and engineers like you bring their vision to life, present their designs to their clients, and deliver their projects to the community.
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DLRM
An implementation of a deep learning recommendation model (DLRM)
...The implementation is optimized for performance at scale, supporting multi-GPU and multi-node execution, quantization, embedding partitioning, and pipelined I/O to feed huge embeddings efficiently. It includes data loaders for standard benchmarks (like Criteo), training scripts, evaluation tools, and capabilities like mixed precision, gradient compression, and memory fusion to maximize throughput. -
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MoCo (Momentum Contrast)
Self-supervised visual learning using momentum contrast in PyTorch
MoCo is an open source PyTorch implementation developed by Facebook AI Research (FAIR) for the papers “Momentum Contrast for Unsupervised Visual Representation Learning” (He et al., 2019) and “Improved Baselines with Momentum Contrastive Learning” (Chen et al., 2020). It introduces Momentum Contrast (MoCo), a scalable approach to self-supervised learning that enables visual representation learning without labeled data. The core idea of MoCo is to maintain a dynamic dictionary with a momentum-updated encoder, allowing efficient contrastive learning across large batches. The repository includes implementations for both MoCo v1 and MoCo v2, the latter improving training stability and performance through architectural and augmentation enhancements. ... -
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DeepCTR-Torch
Easy-to-use,Modular and Extendible package of deep-learning models
DeepCTR-Torch is an easy-to-use, Modular and Extendible package of deep-learning-based CTR models along with lots of core components layers that can be used to build your own custom model easily.It is compatible with PyTorch.You can use any complex model with model.fit() and model.predict(). With the great success of deep learning, DNN-based techniques have been widely used in CTR estimation tasks. The data in the CTR estimation task usually includes high sparse,high cardinality categorical features and some dense numerical features. Low-order Extractor learns feature interaction through product between vectors. Factorization-Machine and it’s variants are widely used to learn the low-order feature interaction. High-order Extractor learns feature combination through complex neural network functions like MLP, Cross Net, etc. -
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DGL
Python package built to ease deep learning on graph
...We also want to make the combination of graph based modules and tensor based modules (PyTorch or MXNet) as smooth as possible. DGL provides a powerful graph object that can reside on either CPU or GPU. It bundles structural data as well as features for a better control. We provide a variety of functions for computing with graph objects including efficient and customizable message passing primitives for Graph Neural Networks. -
Attack Surface Management | Criminal IP ASMCriminal IP’s Attack Surface Management (ASM) is a threat-intelligence–driven platform that continuously discovers, inventories, and monitors every internet-connected asset associated with an organization, including shadow and forgotten resources, so teams see their true external footprint from an attacker’s perspective. The solution combines automated asset discovery with OSINT techniques, AI enrichment and advanced threat intelligence to surface exposed hosts, domains, cloud services, IoT endpoints and other Internet-facing vectors, capture evidence (screenshots and metadata), and correlate findings to known exploitability and attacker tradecraft. ASM prioritizes exposures by business context and risk, highlights vulnerable components and misconfigurations, and provides real-time alerts and dashboards to speed investigation and remediation.
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PyTorch/XLA
Enabling PyTorch on Google TPU
PyTorch/XLA is a Python package that uses the XLA deep learning compiler to connect the PyTorch deep learning framework and Cloud TPUs. You can try it right now, for free, on a single Cloud TPU with Google Colab, and use it in production and on Cloud TPU Pods with Google Cloud. Take a look at one of our Colab notebooks to quickly try different PyTorch networks running on Cloud TPUs and learn how to use Cloud TPUs as PyTorch devices. We are also introducing new TPU VMs for more transparent... -
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Luminal
Deep learning at the speed of light
Luminal is a framework designed to accelerate and simplify the development of systems-level data applications by using a typed, functional, and streaming-first approach. Instead of treating data processing as a series of ad-hoc scripts, Luminal models transformations as strongly typed building blocks that can be composed into reliable, scalable pipelines. The project emphasizes correctness and performance by requiring explicit types for the data flowing through transformations, reducing runtime surprises and allowing for highly optimized execution. ... -
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Deep Learning Models
A collection of various deep learning architectures, models, and tips
This repository collects clear, well-documented implementations of deep learning models and training utilities written by Sebastian Raschka. The code favors readability and pedagogy: components are organized so you can trace data flow through layers, losses, optimizers, and evaluation. Examples span fundamental architectures—MLPs, CNNs, RNN/Transformers—and practical tasks like image classification or text modeling. Reproducible training scripts and configuration files make it straightforward to rerun experiments or adapt them to your own datasets. The repo often pairs implementations with notes on design choices and trade-offs, turning it into both a toolbox and a learning resource. ... -
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CometAnalyser
CometAnalyser, for quantitative comet assay analysis.
Description: Comet assay provides an easy solution to estimate DNA damage in single cells through microscopy assessment. To obtain reproducible and reliable quantitative data, we developed an easy-to-use tool named CometAnalyser. CometAnalyser is an open-source deep-learning tool designed for the analysis of both fluorescent and silver-stained wide-field microscopy images. Once the comets are segmented and classified, several intensity/morphological features are automatically exported as a spreadsheet file. ... -
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Lightning-Hydra-Template
PyTorch Lightning + Hydra. A very user-friendly template
...A collection of best practices for efficient workflow and reproducibility. Thoroughly commented - you can use this repo as a reference and educational resource. Not fitted for data engineering - the template configuration setup is not designed for building data processing pipelines that depend on each other. PyTorch Lightning, a lightweight PyTorch wrapper for high-performance AI research. Think of it as a framework for organizing your PyTorch code. Hydra, a framework for elegantly configuring complex applications. ... -
Simplify Time-consuming and Overly Complicated Financial Processes.Zahara's cloud based platform automates budget management, suppliers, purchase requisitions, multi-level purchase approvals, deliveries and invoice reconciliation and approvals. Zahara integrates with most leading accounting software such as QuickBooks Online and Xero to give expanding SME's real time visibility and centralized control of their purchasing. Zahara can be used to control spend in an organization. We take the initial request to buy something and automate the approval process and sending of the PO to the Vendor. Deliveries can be receipted, vendors invoices matched and processed and then exported to finance. Zahara adds control yet speeds up processing.
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fastMRI
A large open dataset + tools to speed up MRI scans using ML
...By enabling reconstruction of high-fidelity MR images from significantly fewer measurements, fastMRI aims to make MRI scanning faster, cheaper, and more accessible in clinical settings. The repository provides an open-source PyTorch framework with data loaders, subsampling utilities, reconstruction models, and evaluation metrics, supporting both research reproducibility and practical experimentation. It includes reference implementations for key MRI reconstruction architectures such as U-Net and Variational Networks (VarNet), along with example scripts for model training and evaluation using the PyTorch Lightning framework. ... -
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Horovod
Distributed training framework for TensorFlow, Keras, PyTorch, etc.
...Horovod can be installed on-premise or run out-of-the-box in cloud platforms, including AWS, Azure, and Databricks. Horovod can additionally run on top of Apache Spark, making it possible to unify data processing and model training into a single pipeline. Once Horovod has been configured, the same infrastructure can be used to train models with any framework, making it easy to switch between TensorFlow, PyTorch, MXNet, and future frameworks as machine learning tech stacks continue to evolve. Start scaling your model training with just a few lines of Python code. ... -
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TensorFlow Ranking
Learning to rank in TensorFlow
...Multi-item (also known as groupwise) scoring functions. LambdaLoss implementation for direct ranking metric optimization. Unbiased Learning-to-Rank from biased feedback data. We envision that this library will provide a convenient open platform for hosting and advancing state-of-the-art ranking models based on deep learning techniques, and thus facilitate both academic research and industrial applications. We provide a demo, with no installation required, to get started on using TF-Ranking. This demo runs on a colaboratory notebook, an interactive Python environment. ... -
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Spektral
Graph Neural Networks with Keras and Tensorflow 2
...You can use Spektral for classifying the users of a social network, predicting molecular properties, generating new graphs with GANs, clustering nodes, predicting links, and any other task where data is described by graphs. Spektral implements some of the most popular layers for graph deep learning. Spektral also includes lots of utilities for representing, manipulating, and transforming graphs in your graph deep learning projects. Spektral is compatible with Python 3.6 and above, and is tested on the latest versions of Ubuntu, MacOS, and Windows. ... -
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AI-Agent-Host
The AI Agent Host is a module-based development environment.
...Being data-aware involves connecting a language model to other sources of data, enabling a comprehensive understanding and analysis of information. -
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DIG
A library for graph deep learning research
...If you are working or plan to work on research in graph deep learning, DIG enables you to develop your own methods within our extensible framework, and compare with current baseline methods using common datasets and evaluation metrics without extra efforts. It includes unified implementations of data interfaces, common algorithms, and evaluation metrics for several advanced tasks. Our goal is to enable researchers to easily implement and benchmark algorithms. -
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T81 558
Applications of Deep Neural Networks
...Application of these architectures to computer vision, time series, security, natural language processing (NLP), and data generation will be covered. High-Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well as grids. -
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AllenNLP
An open-source NLP research library, built on PyTorch
AllenNLP makes it easy to design and evaluate new deep learning models for nearly any NLP problem, along with the infrastructure to easily run them in the cloud or on your laptop. AllenNLP includes reference implementations of high quality models for both core NLP problems (e.g. semantic role labeling) and NLP applications (e.g. textual entailment). AllenNLP supports loading "plugins" dynamically. A plugin is just a Python package that provides custom registered classes or additional... -
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OmicSelector
Feature selection and deep learning modeling for omic biomarker study
OmicSelector is an environment, Docker-based web application, and R package for biomarker signature selection (feature selection) from high-throughput experiments and others. It was initially developed for miRNA-seq (small RNA, smRNA-seq; hence the name was miRNAselector), RNA-seq and qPCR, but can be applied for every problem where numeric features should be selected to counteract overfitting of the models. Using our tool, you can choose features, like miRNAs, with the most significant... -
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Elephas
Distributed Deep learning with Keras & Spark
...Elephas brings deep learning with Keras to Spark. Elephas intends to keep the simplicity and high usability of Keras, thereby allowing for fast prototyping of distributed models, which can be run on massive data sets. Elephas implements a class of data-parallel algorithms on top of Keras, using Spark's RDDs and data frames. Keras Models are initialized on the driver, then serialized and shipped to workers, alongside with data and broadcasted model parameters. Spark workers deserialize the model, train their chunk of data and send their gradients back to the driver. ... -
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Auto-PyTorch
Automatic architecture search and hyperparameter optimization
...To bring the best of these two worlds together, we developed Auto-PyTorch, which jointly and robustly optimizes the network architecture and the training hyperparameters to enable fully automated deep learning (AutoDL). Auto-PyTorch is mainly developed to support tabular data (classification, regression) and time series data (forecasting). The newest features in Auto-PyTorch for tabular data are described in the paper "Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL" (see below for bibtex ref). Details about Auto-PyTorch for multi-horizontal time series forecasting tasks can be found in the paper "Efficient Automated Deep Learning for Time Series Forecasting" (also see below for bibtex ref). -
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AirSim
A simulator for drones, cars and more, built on Unreal Engine
...AirSim's development is oriented towards the goal of creating a platform for AI research to experiment with deep learning, computer vision and reinforcement learning algorithms for autonomous vehicles. For this purpose, AirSim also exposes APIs to retrieve data and control vehicles in a platform independent way. AirSim is fully enabled for multiple vehicles. This capability allows you to create multiple vehicles easily and use APIs to control them. -
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tf2_course
Notebooks for my "Deep Learning with TensorFlow 2 and Keras" course
tf2_course provides the notebooks for the “Deep Learning with TensorFlow 2 and Keras” course authored by the same author, Aurélien Géron. It is structured as a teaching toolkit: you’ll find notebooks covering neural networks with Keras, lower-level TensorFlow APIs, data loading & preprocessing, convolutional and recurrent networks, and deployment/distribution of models. The material is intended for learners who already have foundational knowledge of ML and wish to deepen their understanding of deep learning frameworks and practices. The repo supports experimentation: you can run code, tweak hyperparameters, and follow guided exercises that strengthen practical mastery. ... -
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Catalyst
Accelerated deep learning R&D
Catalyst is a PyTorch framework for accelerated Deep Learning research and development. It allows you to write compact but full-featured Deep Learning pipelines with just a few lines of code. With Catalyst you get a full set of features including a training loop with metrics, model checkpointing and more, all without the boilerplate. Catalyst is focused on reproducibility, rapid experimentation, and codebase reuse so you can break the cycle of writing another regular train loop and make... -
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LayoutParser
A Unified Toolkit for Deep Learning Based Document Image Analysis
With the help of state-of-the-art deep learning models, Layout Parser enables extracting complicated document structures using only several lines of code. This method is also more robust and generalizable as no sophisticated rules are involved in this process. A complete instruction for installing the main Layout Parser library and auxiliary components. Learn how to load DL Layout models and use them for layout detection. The full list of layout models currently available in Layout Parser.... -
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Deep Learning course
Slides and Jupyter notebooks for the Deep Learning lectures
Slides and Jupyter notebooks for the Deep Learning lectures at Master Year 2 Data Science from Institut Polytechnique de Paris. This course is being taught at as part of Master Year 2 Data Science IP-Paris. Note: press "P" to display the presenter's notes that include some comments and additional references. This lecture is built and maintained by Olivier Grisel and Charles Ollion.
