Search Results for "open-shell" - Page 17
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Showing 1154 open source projects for "open-shell"
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tika-python
Python binding to the Apache Tika™ REST services
A Python port of the Apache Tika library that makes Tika available using the Tika REST Server. This makes Apache Tika available as a Python library, installable via Setuptools, Pip and easy to install. To use this library, you need to have Java 7+ installed on your system as tika-python starts up the Tika REST server in the background. To get this working in a disconnected environment, download a tika server file (both tika-server.jar and tika-server.jar.md5, which can be found here) and set... -
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fastai
Deep learning library
fastai is a deep learning library which provides practitioners with high-level components that can quickly and easily provide state-of-the-art results in standard deep learning domains, and provides researchers with low-level components that can be mixed and matched to build new approaches. It aims to do both things without substantial compromises in ease of use, flexibility, or performance. This is possible thanks to a carefully layered architecture, which expresses common underlying... -
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LRSLibrary
Low-Rank and Sparse Tools for Background Modeling and Subtraction
...The algorithms can also be adapted to other computer vision or machine learning problems beyond video. Large algorithm collection: > 100 matrix- and tensor-based low-rank + sparse methods. Open-source license, documentation and references included. -
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Darts
A python library for easy manipulation and forecasting of time series
darts is a Python library for easy manipulation and forecasting of time series. It contains a variety of models, from classics such as ARIMA to deep neural networks. The models can all be used in the same way, using fit() and predict() functions, similar to scikit-learn. The library also makes it easy to backtest models, combine the predictions of several models, and take external data into account. Darts supports both univariate and multivariate time series and models. The ML-based models... -
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SpikingJelly
SpikingJelly is an open-source deep learning framework
SpikingJelly is an open-source deep learning framework for spiking neural networks that is primarily built on top of PyTorch and aimed at neuromorphic computing research. The project provides the components needed to build, train, and evaluate neural models that communicate through discrete spikes rather than the continuous activations used in conventional artificial neural networks. -
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AI_Tutorial
A selection of learning materials, search, recommendation, advertising
...The project functions as a centralized knowledge base designed to help engineers and researchers discover tutorials, technical articles, algorithm explanations, and architecture discussions from across the AI ecosystem. Rather than focusing on a single framework or course, the repository collects materials from many sources such as open-source projects, technical blogs, research papers, and industry engineering posts. The curated content includes topics like recommendation systems, search engine architecture, neural networks, graph neural networks, and modern deep learning techniques. The goal of the project is to reduce information fragmentation by organizing valuable AI resources into structured sections that can be explored easily by learners and practitioners. -
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Machine Learning for Software Engineers
A complete daily plan for studying to become a machine learning engine
Machine Learning for Software Engineers is an open-source learning roadmap designed to help software engineers transition into machine learning roles through a structured, practical study plan. The repository presents a top-down learning path that emphasizes hands-on experience rather than heavy theoretical prerequisites, making it particularly approachable for developers who already have programming experience but limited formal training in machine learning. -
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DeepDetect
Deep Learning API and Server in C++14 support for Caffe, PyTorch
...Neural network templates for the most effective architectures for GPU, CPU, and Embedded devices. Training in a few hours and with small data thanks to 25+ pre-trained models. Full Open Source, with an ecosystem of tools (API clients, video, annotation, ...) Fast Server written in pure C++, a single codebase for Cloud, Desktop & Embedded. -
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cracking-the-data-science-interview
A Collection of Cheatsheets, Books, Questions, and Portfolio
Cracking the Data Science Interview is an open educational repository that collects study materials, resources, and reference links for preparing for data science interviews. The project organizes content across many fundamental areas of data science, including statistics, probability, SQL, machine learning, and deep learning. It includes cheat sheets that summarize important technical concepts commonly discussed during technical interviews. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Deep Learning Interviews book
Hundreds of fully solved job interview questions
The interviews.ai repository hosts the open materials for the book Deep Learning Interviews, a comprehensive collection of technical questions and fully solved problems covering many aspects of artificial intelligence. The project was created to help students, researchers, and engineers prepare for machine learning and deep learning interviews by providing structured explanations of key concepts. -
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machine_learning_examples
A collection of machine learning examples and tutorials
machine_learning_examples is an open-source repository that provides a large collection of machine learning tutorials and practical code examples. The project aims to teach machine learning concepts through hands-on programming rather than purely theoretical explanations. It includes implementations of many machine learning algorithms and neural network architectures using Python and popular libraries such as TensorFlow and NumPy. -
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ML-NLP
This project is a common knowledge point and code implementation
ML-NLP is a large open-source repository that collects theoretical knowledge, practical explanations, and code examples related to machine learning, deep learning, and natural language processing. The project is designed primarily as a learning resource for algorithm engineers and students preparing for technical interviews in machine learning or NLP roles. -
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OpenMLSys-ZH
Machine Learning Systems: Design and Implementation
...The repo mirrors the structure of the original OpenMLSys docs: sections on system design, API references, deployment instructions, module overviews, and example workflows. It helps bridge language barriers in open machine learning systems by providing side-by-side translation or localized explanations. The repository includes scripts or tooling to keep translation synchronized with upstream changes, versioning, and possibly translation metadata (contributors, timestamp). Users can browse or clone the translated documentation to follow along with the original content, deploy examples, or understand system internals in their preferred language. -
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Zero to Mastery Machine Learning
All course materials for the Zero to Mastery Machine Learning
Zero to Mastery Machine Learning is an open-source repository that contains the complete course materials for the Zero to Mastery Machine Learning and Data Science bootcamp. The project provides a structured curriculum designed to teach machine learning and data science using Python through hands-on projects and interactive notebooks. The repository includes datasets, Jupyter notebooks, documentation, and example code that walk learners through the entire machine learning workflow from problem definition to model deployment. ... -
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CUDA Containers for Edge AI & Robotics
Machine Learning Containers for NVIDIA Jetson and JetPack-L4T
CUDA Containers for Edge AI & Robotics is an open-source project that provides a modular container build system designed for running machine learning and AI workloads on NVIDIA Jetson devices. The repository contains container configurations that package the latest AI frameworks and dependencies optimized for Jetson hardware. These containers simplify the deployment of complex machine learning environments by bundling libraries such as CUDA, TensorRT, and deep learning frameworks into reproducible container images. ... -
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Machine Learning Zoomcamp
Learn ML engineering for free in 4 months
Machine Learning Zoomcamp is an open-source repository containing the materials for a comprehensive course that teaches machine learning engineering from fundamentals to deployment. The project is designed to guide learners through the complete lifecycle of developing machine learning systems, starting with data preparation and model training and ending with production deployment. -
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CVPR 2025
Collection of CVPR 2025 papers and open source projects
CVPR 2025 curates accepted CVPR 2025 papers and pairs them with their corresponding code implementations when available, giving researchers and practitioners a fast way to move from reading to reproducing. It organizes entries by topic areas such as detection, segmentation, generative models, 3D vision, multi-modal learning, and efficiency, so you can navigate the year’s output efficiently. Each paper entry typically includes a title, author list, and links to the paper PDF and official or... -
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DeepChem
Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, etc
DeepChem aims to provide a high-quality open-source toolchain that democratizes the use of deep learning in drug discovery, materials science, quantum chemistry, and biology. DeepChem currently supports Python 3.7 through 3.9 and requires these packages on any condition. DeepChem has a number of "soft" requirements. If you face some errors like ImportError: This class requires XXXX, you may need to install some packages. -
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Django friendly finite state machine
Django friendly finite state machine support
Django-fsm adds simple declarative state management for Django models. If you need parallel task execution, view, and background task code reuse over different flows - check my new project Django-view flow. Instead of adding a state field to a Django model and managing its values by hand, you use FSMField and mark model methods with the transition decorator. These methods could contain side effects of the state change. You may also take a look at the Django-fsm-admin project containing a... -
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Transformers in Time Series
A professionally curated list of awesome resources
Transformers in Time Series is a curated research repository that collects academic papers, code implementations, datasets, and learning resources related to transformer models for time series analysis. The project was created to systematically organize the rapidly growing research field that applies transformer architectures to time series modeling tasks. It compiles literature from major conferences and journals and categorizes them by application domains such as forecasting, anomaly... -
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MLPerf
Reference implementations of MLPerf™ training benchmarks
This is a repository of reference implementations for the MLPerf training benchmarks. These implementations are valid as starting points for benchmark implementations but are not fully optimized and are not intended to be used for "real" performance measurements of software frameworks or hardware. Benchmarking the performance of training ML models on a wide variety of use cases, software, and hardware drives AI performance across the tech industry. The MLPerf Training working group draws on... -
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higgsfield
Fault-tolerant, highly scalable GPU orchestration
Higgsfield is an open-source, fault-tolerant, highly scalable GPU orchestration, and a machine learning framework designed for training models with billions to trillions of parameters, such as Large Language Models (LLMs). -
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A Survey of Surveys
A collection of 1000+ survey papers on Natural Language Processing
A Survey of Surveys is a large curated repository that collects and organizes survey papers related to natural language processing, machine learning, and artificial intelligence research. The project aims to provide a centralized index of survey literature that summarizes major developments across different subfields of AI. Rather than focusing on code implementations, the repository functions as an academic resource that helps researchers quickly discover comprehensive survey papers... -
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snntorch
Deep and online learning with spiking neural networks in Python
snntorch is a deep learning library that enables researchers and developers to build and train spiking neural networks using the PyTorch framework. Spiking neural networks are biologically inspired models that communicate through discrete spike events rather than continuous activation values, making them closer to how neurons operate in the brain. The library extends PyTorch’s tensor computation capabilities to support gradient-based learning for networks composed of spiking neurons. This... -
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OneFlow
OneFlow is a deep learning framework designed to be user-friendly
OneFlow is a deep learning framework designed to be user-friendly, scalable and efficient. An extension for OneFlow to target third-party compiler, such as XLA, TensorRT and OpenVINO etc.CUDA runtime is statically linked into OneFlow. OneFlow will work on a minimum supported driver, and any driver beyond. For more information. Distributed performance (efficiency) is the core technical difficulty of the deep learning framework. OneFlow focuses on performance improvement and heterogeneous...
