Open Source Libraries - Page 77
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DeckTransition
A library to recreate the iOS Apple Music now playing transition
DeckTransition is an attempt to recreate the card-like transition found in the iOS 10 Apple Music and iMessage apps. The transition can be called from code or using a storyboard. To use via storyboards, just setup a custom segue (kind set to custom), and set the class to DeckSegue. Set modalPresentationCapturesStatusBarAppearance to true in your modal view controller, and override the preferredStatusBarStyle variable to return .lightContent. By default, DeckTransition has a swipe-to-dismiss gesture which is automatically enabled when your modalʼs main UIScrollView is scrolled to the top. DeckTransition has an internal heuristic to determine which UIScrollView should be tracked for the swipe-to-dismiss gesture. In general, this should be sufficient for and cover most use cases. -
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Decompose
Kotlin Multiplatform lifecycle-aware business logic components
Decompose is a Kotlin Multiplatform library for breaking down your code into tree-structured lifecycle-aware business logic components (aka BLoC), with routing functionality and pluggable UI (Jetpack/Multiplatform Compose, Android Views, SwiftUI, Kotlin/React, etc.). -
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Deep Learning Essay Reading
Read classic and new deep learning papers paragraph by paragraph
Deep Learning Essay Reading repository is a comprehensive collection of machine learning and deep learning research summaries designed to make cutting-edge academic work more accessible. Instead of reading entire dense academic papers, contributors provide structured breakdowns and insights into the most influential research from the past decade, often including explanation highlights and key takeaways. The content spans foundational models, architectures, and training methodologies across computer vision, natural language processing, generative models, and other machine learning domains. These summaries help students, researchers, and engineers stay up to date with breakthroughs in the field without needing to sift through full academic documents. With thousands of stars and forks, this repository has become a widely referenced learning resource for anyone interested in understanding the technical ideas behind major advancements. -
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DeepDream
This repository contains IPython Notebook with sample code
DeepDream is a small, educational repository that accompanies Google’s original “Inceptionism” blog post by providing a runnable IPython/Jupyter notebook that demonstrates how to “dream” through a convolutional neural network. The notebook shows how to take a trained vision model and iteratively amplify patterns the network detects, producing the hallmark surreal, hallucinatory visuals. It walks through loading a pretrained network, selecting layers and channels to maximize, computing gradients with respect to the input image, and applying multi-scale “octave” processing to reveal fine and coarse patterns. The code is intentionally compact and exploratory, encouraging users to tweak layers, step sizes, and scales to influence the aesthetic. Although minimal, it illustrates important concepts like feature visualization, activation maximization, and the effect of different receptive fields on the final image. -
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DeepEP
DeepEP: an efficient expert-parallel communication library
DeepEP is a communication library designed specifically to support Mixture-of-Experts (MoE) and expert parallelism (EP) deployments. Its core role is to implement high-throughput, low-latency all-to-all GPU communication kernels, which handle the dispatching of tokens to different experts (or shards) and then combining expert outputs back into the main data flow. Because MoE architectures require routing inputs to different experts, communication overhead can become a bottleneck — DeepEP addresses that by providing optimized GPU kernels and efficient dispatch/combining logic. The library also supports low-precision operations (such as FP8) to reduce memory and bandwidth usage during communication. DeepEP is aimed at large-scale model inference or training systems where expert parallelism is used to scale model capacity without replicating entire networks. -
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DeepLearning
Deep Learning (Flower Book) mathematical derivation
" Deep Learning " is the only comprehensive book in the field of deep learning. The full name is also called the Deep Learning AI Bible (Deep Learning) . It is edited by three world-renowned experts, Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Includes linear algebra, probability theory, information theory, numerical optimization, and related content in machine learning. At the same time, it also introduces deep learning techniques used by practitioners in the industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling and practical methods, and investigates topics such as natural language processing, Applications in speech recognition, computer vision, online recommender systems, bioinformatics, and video games. Finally, the Deep Learning book provides research directions covering theoretical topics including linear factor models, autoencoders, representation learning, structured probabilistic models, etc. -
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DeepLinkDispatch
Annotation-based library for making deep link handling better
Deep links provide a way to link to specific content on either a website or an application. These links are indexable and searchable, and can provide users direct access to much more relevant information than a typical home page or screen. In the mobile context, the links are URIs that link to specific locations in the application. At Airbnb, we use these deep links frequently to link to listings, reservations, or search queries. Android supports deep links through declaration in the Manifest. You can add an intent filters which define a mapping between deep link schemas and Activities. Subsequently, any URI with the registered scheme, host, and path will open up that Activity in the app. You can’t easily indicate the parameters that you would expect in the URI that you are filtering for. For complex deep links, you are likely to have to write a parsing mechanism to extract out the parameters, or worse, have such similar code distributed amongst many Activities. -
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DeepMind Research
Implementations and code to accompany DeepMind publications
This repository collects reference implementations and illustrative code accompanying a wide range of DeepMind publications, making it easier for the research community to reproduce results, inspect algorithms, and build on prior work. The top level organizes many paper-specific directories across domains such as deep reinforcement learning, self-supervised vision, generative modeling, scientific ML, and program synthesis—for example BYOL, Perceiver/Perceiver IO, Enformer for genomics, MeshGraphNets for physics, RL Unplugged, Nowcasting for weather, and more. Each project folder typically includes its own README, scripts, and notebooks so you can run experiments or explore models in isolation, and many link to associated datasets or external environments like DeepMind Lab and StarCraft II. The codebase is primarily Jupyter Notebooks and Python, reflecting an emphasis on experimentation and pedagogy rather than production packaging. -
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DeepPavlov
A library for deep learning end-to-end dialog systems and chatbots
DeepPavlov makes it easy for beginners and experts to create dialogue systems. The best place to start is with user-friendly tutorials. They provide quick and convenient introduction on how to use DeepPavlov with complete, end-to-end examples. No installation needed. Guides explain the concepts and components of DeepPavlov. Follow step-by-step instructions to install, configure and extend DeepPavlov framework for your use case. DeepPavlov is an open-source framework for chatbots and virtual assistants development. It has comprehensive and flexible tools that let developers and NLP researchers create production-ready conversational skills and complex multi-skill conversational assistants. Use BERT and other state-of-the-art deep learning models to solve classification, NER, Q&A and other NLP tasks. DeepPavlov Agent allows building industrial solutions with multi-skill integration via API services. -
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DeepXDE
A library for scientific machine learning & physics-informed learning
DeepXDE is a library for scientific machine learning and physics-informed learning. DeepXDE includes the following algorithms. Physics-informed neural network (PINN). Solving different problems. Solving forward/inverse ordinary/partial differential equations (ODEs/PDEs) [SIAM Rev.] Solving forward/inverse integro-differential equations (IDEs) [SIAM Rev.] fPINN: solving forward/inverse fractional PDEs (fPDEs) [SIAM J. Sci. Comput.] NN-arbitrary polynomial chaos (NN-aPC): solving forward/inverse stochastic PDEs (sPDEs) [J. Comput. Phys.] PINN with hard constraints (hPINN): solving inverse design/topology optimization [SIAM J. Sci. Comput.] Residual-based adaptive sampling [SIAM Rev., arXiv] Gradient-enhanced PINN (gPINN) [Comput. Methods Appl. Mech. Eng.] PINN with multi-scale Fourier features [Comput. Methods Appl. Mech. Eng.] -
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Deepend
Deallocate groups of objects in a single call
Deepend is a storage pool with subpool capabilities for Ada 2022, Ada 2012, Ada 2005, and Ada 95. Memory allocations can be associated with subpools and subpools can be deallocated as a whole which provides a safer alternative than managing deletions of individual objects. It also is likely to be more deterministic and efficient than garbage collection. -
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Deeplearning-papernotes
Summaries and notes on Deep Learning research papers
Deeplearning-papernotes is an implementation of Convolutional Neural Networks for sentence and text classification in TensorFlow, based on a well-known research paper that applies CNN architectures to natural language processing tasks with strong performance in sentiment analysis and similar classification problems. The repository provides the complete network definition, including an embedding layer to convert words into dense representations, convolution and max-pooling layers to extract informative features, and a final softmax classifier to distinguish between target classes. It includes data preprocessing helpers, training scripts, and configuration options so developers can experiment with different filter sizes, dropout rates, and hyperparameters to optimize performance for their dataset. -
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Deequ
Deequ is a library built on top of Apache Spark
Deequ is a library built atop Apache Spark that enables defining “unit tests for data” — that is, formal constraints or checks on datasets to ensure data quality along dimensions such as completeness, uniqueness, value ranges, correlations, etc. It can scale to large datasets (billions of rows) by translating those data checks into Spark jobs. Deequ supports advanced features like a metrics repository for storing computed statistics over time, anomaly detection of data quality metrics, and the suggestion of likely constraints automatically for new datasets. It also includes a little domain-specific language called DQDL (Data Quality Definition Language) which allows declarative specification of quality rules. Users typically run Deequ before feeding data downstream (to ML pipelines, analytics, or production systems), enabling early detection and isolation of data errors. There is also a Python wrapper, PyDeequ, for users who prefer working from Python environments. -
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Default composer
JavaScript library to set default values for nested objects
A tiny (~500B) JavaScript library that allows you to set default values for nested objects. "default-composer" is a JavaScript library that allows you to set default values for nested objects. The library replaces empty strings/arrays/objects, null, or undefined values in an existing object with the defined default values, which helps simplify programming logic and reduce the amount of code needed to set default values. To use "default-composer", simply import the library and call the defaultComposer() function with the default values object and the original object that you want to set default values for. -
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An Error Handler for C/C++ funtions, focused on code readability. It allows a clear control flow, adding Ignore, Retry, Jump, and Back functionality when a function returns error. It's macro based, written in C and targeting embedded systems.
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Delphi : VRCalc++ OOSL (Script) and more
Delphi : VRCalc++ OOSL & + (Paged List, TextEditor, VRAstroVision ...)
Vincent Radio {Adrix.NT} Sources Library & Applications : Delphi C++ Java VRCalc++ C# VRCalc++ Object Oriented Scripting Language - Engine Source Pascal Code - Delphi Packages Build Prjs - VRCalc++ Scripted System Std RT Library - Guides & Docs (CHM, PDF, DOCX) - VCL & FMX (FireMonkey) Support - Script Test Code (Lang RTL VCL FMX) - Visual Stage Project : VCL & FMX Paged Lists & Iterators : Delphi C++ Java C# Multi-Dim Arrays & Direct Graph Classes : Delphi C++ Java VRCalc++ C# Delphi Drag&Drop Applications - VRCalc++ Script Executors: Terminal, VCL, FMX - VRMultiEdit - VRLazyCodeEditor - VR Astro Vision (Astrology) - Paged List Test - VRMosaic : Delphi C++Builder Java C# +with auto resolver - VR Free Chess 2D - VRBlocks - VRGraphStage - VR TTT OX + icons & bitmaps + VCL VisualStyles + Build Projects + instructions to build projects to build projects - Delphi : RAD Studio - C++ : Dev-C++ - java : NetBeans - C# : MS/VS adrixnt@hotmail.it -
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Delphi Drag&Drop Suite by Adrix.NT
Delphi Drag & Drop Comp Suite v.5.2 rev. by Vincent Radio {Adrix.NT}
The Delphi Drag and Drop Component Suite v5.2 released Drag & Drop Delphi Library (*) Sources & Build Projects included original designer: Anders Melander www: melander.dk Revised & Adapted by Vincent Radio {Adrix.NT} ________________________________________________________________________________ REVISION NOTES the original Library from Anders Melander was splitted into 2 separed packages - a Run Time Package: "VRAxDragDropRTLCompSuitePackage" - a Design Time Package: "VRAxDragDropCompSuiteDesignPkg" (Win32 only) this way you can also use it on your 64 bits (Win64) application and install the design time package (Win32) into Delphi IDE which is a 32 bits Applic !! have fun with Drag & Drop for more info please refer to ... MelanderBlog: melander.dk - The Drag and Drop Component Suite v5.2 released for any question about installation e-mail me at: adrixnt@hotmail.it PS: it seems not working in C++Builder !! Adrix.NT -
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A Delphi class providing a set of functions to calculate prayer times based on a variety of conventions usually used in Muslim communities. It is based on Hamid Zarrabi-Zadeh's PrayTimes library which is in JavaScript. (http://tanzil.info/praytime).
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Depeche
Java database mapper
Depeche enables you to work with databases using data structures. It strives to be very simple and easy to use, avoiding both the tediousness of using JDBC directly and the heaviness of a full-blown ORM. It currently supports PostgreSQL, MySQL, H2, SQLite, MS SQL Server and FileMaker; support for other databases can be added easily. A simple ORM based on Depeche is also under development. -
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Design Patterns Library
A comprehensive design patterns library implemented in C#
A comprehensive design patterns library implemented in C#, which covers various design patterns from the most commonly used ones to the lesser-known ones. Get familiar with and learn design patterns through moderately realistic examples. In software engineering, a design pattern is a general repeatable solution to a commonly occurring problem in software design. A design pattern isn't a finished design that can be transformed directly into code. It is a description or template for how to solve a problem that can be used in many different situations. In addition, design patterns allow developers to communicate using well-known, well-understood names for software interactions. Know when to use a certain design pattern, and when not to. No design pattern is a 42 - the answer to life, the universe and everything. There are situations in which every design pattern easily becomes an antipattern. -
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Design Patterns Written in Unity3D
All Gang of Four Design Patterns written in Unity C#
The Design Patterns Written in Unity3D project is a guide to implementing software design patterns specifically within the Unity game development environment. It demonstrates how common patterns such as singleton, observer, factory, and state can be adapted to Unity’s architecture. The repository includes practical examples and code snippets that show how to structure game systems for scalability and maintainability. It emphasizes clean code practices and modular design, helping developers build more robust and reusable components. The project also explains the reasoning behind each pattern, making it educational as well as practical. By focusing on Unity-specific use cases, it addresses challenges unique to game development workflows. Overall, it provides a valuable reference for improving code quality in Unity projects. -
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Design Patterns in Swift
Design Patterns implemented in Swift
Design-Patterns-In-Swift is a repository that translates classic software engineering design patterns (from sources like the Gang of Four) into Swift code examples, so you can see how those patterns look in a modern, strongly typed, object-/protocol-oriented language. It covers creational, structural, and behavioral patterns: singletons, factories, decorators, observers, strategy, command, mediator, and more. For each pattern, you’ll typically see one or more Swift implementations, commentary on where it makes sense in a Swift architecture, and caveats about when you might prefer pure protocol composition or using functional patterns instead. The examples aim to be readable and idiomatic—not overly abstract or contrived—so developers can adopt or adapt them directly. -
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Graphical library focused on tridimensional rendering with OpenGL standards. High performance and scalability are the goal of this project.
