Search Results for "theory"
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Showing 31 open source projects for "theory"
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Cytoscape.js
Graph theory library for visualization and analysis
A fully featured graph library written in pure JS. Permissive open source license (MIT) for the core Cytoscape.js library and all first-party extensions. Used in commercial projects and open-source projects in production. Designed for users first, for both frontfacing app usecases and developer usecases. Highly optimized. Compatible with All modern browsers. Legacy browsers with ES5 and canvas support. ES5 and canvas support are required, and feature detection is used for optional... -
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Proxy
Proxy: Next Generation Polymorphism in C++
...To workaround these drawbacks, some languages like Java or C# choose to sacrifice performance by introducing GC to facilitate lifetime management, and JIT-compile the source code at runtime to generate full metadata. We improved the theory and implemented it as a C++ library without sacrificing performance, proposing to merge it into the C++ standard. -
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Go by Example
1000+ Hand-Crafted Go Examples, Exercises, and Quizzes
...English content is complete, while Spanish and Chinese translations are works in progress. The repository emphasizes learning by doing instead of relying mainly on long-form theory. -
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JavaFamily
Java Interview + Java Study Guide
...The repository also emphasizes interview preparation, making it useful for developers seeking to improve their job prospects. It reflects real-world practices and industry standards, helping users bridge the gap between theory and application. -
Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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Introduction-NLP
Detailed notes on HanLP's new book, "Introduction to Natural Language"
Introduction NLP is a Chinese-language study-notes repository based on the book Introduction to Natural Language Processing by the author of HanLP. It explains NLP concepts in accessible language while pairing theory with practical implementation notes. The material begins with basic concepts and Chinese word segmentation before progressing into statistical sequence models. Later chapters cover part-of-speech tagging, named entity recognition, information extraction, text clustering, text classification, dependency parsing, and deep learning. ... -
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LeetCode Master
About "Code Thoughts" LeetCode Practice Guide: 200 classic questions
...The repository contains detailed explanations, categorized problem sets, and step-by-step reasoning behind solutions, making it an effective study companion for developers preparing for technical interviews. Problems are grouped by topic—such as arrays, linked lists, dynamic programming, greedy algorithms, and graph theory—so learners can focus on specific areas systematically. Each solution is accompanied by clear commentary to explain the thought process, algorithm design, and complexity analysis. The project is continuously updated and widely used by learners as both a reference and a roadmap to progress from beginner to advanced LeetCode practice. ... -
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The Agda standard library
The Agda standard library
...If computational performance is important to you, then perhaps try agda-prelude instead. Agda is a dependently typed programming language. It is an extension of Martin-Löf’s type theory and is the latest in the tradition of languages developed in the programming logic group at Chalmers. Other languages in this tradition are Alf, Alfa, Agda 1, Cayenne. Some other loosely related languages are Coq, Epigram, Idris, and Lean. -
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Machine Learning Tutorials Repository
Dive deep into the realms of Machine Learning and other topics
...The repository also serves as a reference for common patterns and workflows in machine learning development. By combining educational content with hands-on examples, it bridges the gap between theory and practice. -
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Hacking with Swift
The project source code for Hacking with iOS
...Each project is designed to reinforce specific concepts such as UI design, data handling, animations, and app architecture. The material emphasizes hands-on learning, encouraging users to build real applications rather than passively consume theory. It is continuously updated to align with the latest versions of Swift, Xcode, and iOS, ensuring relevance for modern development practices. -
Cut Data Warehouse Costs by 54%BigQuery delivers 54% lower TCO with exabyte scale and flexible pricing. Free migration tools handle the SQL translation automatically.
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F2
An elegant, interactive and flexible charting library for mobile
F2 is an out-of-the-box visualization engine focused on the mobile terminal, oriented to conventional statistical charts, perfectly supporting the H5 environment and compatible with multiple environments (Node, applet), complete graphics grammar theory, to meet your various visualization needs , professional mobile design guidelines to bring you the best mobile graphics experience. Best practices for moving side charts around design, performance and heterogeneous environments. Based on the grammar of graphics, Flexible construction of various charts (50+), complete components, covering various scenarios. ... -
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Measurements.jl
Error propagation calculator and library for physical measurements
...Measurements.jl relieves you from the hassle of propagating uncertainties coming from physical measurements, when performing mathematical operations involving them. The linear error propagation theory is employed to propagate the errors. -
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Scalaz
Principled Functional Programming in Scala
...Scalaz also contributes optics, equality/ordering abstractions, and lawful instances with property-based tests to ensure algebraic laws hold. While the Scala ecosystem now includes sibling projects, Scalaz remains a deep toolbox for principled FP in Scala and a reference point for category-theory-inspired design on the JVM. -
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Awesome Network Analysis
A curated list of awesome network analysis resources
awesome-network-analysis is a curated list of resources focused on network and graph analysis, including libraries, frameworks, visualization tools, datasets, and academic papers. It covers multiple programming languages and domains like sociology, biology, and computer science. This repository serves as a central reference for researchers, analysts, and developers working with network data. -
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Introduction to Programming Principles
Categorized overview of Programming Principles & Patterns
Introduction to Programming Principles is a living catalog of fundamental software development principles collected in one place so developers can reference and teach them easily. It documents well-known concepts like SOLID, DRY, KISS, YAGNI, and others, and explains when and why to apply them. The goal is to move principles from being fuzzy tribal knowledge to explicit, written, teachable guidelines. Because it’s on GitHub, it can evolve as the community suggests new principles,... -
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quantitative
Quantized transactions python3
The “quantitative” repository by Jack-Cherish is a tutorial-style codebase for quantitative trading written in Python — essentially a learning resource that guides users through building algorithmic trading strategies step by step. It’s organized as a sequence of lessons (lesson1, lesson2, etc.), making it approachable for learners who want to understand both theory and practice in quantitative finance. The repo is evidently tied to a popular video series (on Bilibili) that reportedly drew substantial attention, suggesting the material is meant to be both educational and hands-on. The README and associated lessons walk the user through implementing algorithms, likely covering data handling, backtesting, and maybe simple trading logic. ... -
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Awesome Conformal Prediction
A professionally curated list of awesome Conformal Prediction videos
awesome-conformal-prediction is a curated “awesome list” repository on GitHub collecting high-quality resources related to conformal prediction: tutorials, books, papers, theses, open-source libraries, videos, and other educational material. It is not a software library itself but a directory of resources for those wanting to learn or work with conformal prediction and uncertainty quantification. This exceptional resource is the culmination of my PhD journey in Machine Learning, specializing... -
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Advanced CSS Course
Starter files, final projects and FAQ for my Advanced CSS course
...The repository focuses on modern CSS techniques such as Sass, responsive layouts, animations, Flexbox, CSS Grid, and reusable design patterns. It also includes downloadable course slides, making it easier to follow the theory portions alongside the hands-on projects. The code is meant for learning rather than production reuse, but it provides clear examples of how advanced CSS concepts are applied in real interfaces. It is best suited for students who already know basic HTML and CSS and want to move into polished, professional frontend styling. -
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Game-Programmer-Study-Notes
A collection of reading notes from my career as a game programmer
...The repository is structured to guide learners through fundamental and advanced concepts, making it suitable for both beginners and experienced programmers. It includes insights from industry practices as well as academic knowledge, bridging the gap between theory and real-world application. The content is often supplemented with diagrams, examples, and explanations that clarify complex topics. By consolidating resources into a single repository, it reduces the need to search across multiple sources. Overall, it serves as a valuable learning hub for mastering game development concepts. -
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DeepLearning
Deep Learning (Flower Book) mathematical derivation
...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. ... -
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threadandjuc
High performance three-high-import import system
...It is especially useful for Java developers preparing for interviews or improving their understanding of production-level concurrency patterns. Some material also explores large-scale import and high-throughput reporting scenarios, connecting theory to real performance problems. Its main value is giving learners a structured way to study Java concurrency beyond isolated code snippets. -
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Probability Cheatsheet
A comprehensive 10-page probability cheatsheet
The probability_cheatsheet is a cheat sheet repository that summarizes key probability theory concepts, formulas, distributions, and properties in a concise format. It likely includes definitions of random variables, PMFs and PDFs, expectations, variance, common distributions (e.g. binomial, normal, Poisson, exponential), conditional probability, Bayes’ theorem, moment generating functions, and perhaps important inequalities (Markov, Chebyshev, Chernoff). -
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data-science-ipython-notebooks
Data science Python notebooks: Deep learning
...The collection is suitable for self-paced study, quick reference, or as teaching materials in workshops. By combining narrative explanations with executable code, it shortens the path from theory to working prototypes. -
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PyTorch Book
PyTorch tutorials and fun projects including neural talk
...Legacy code has better python2/python3 compatibility, CPU/GPU compatibility test. The new version of the code has not been fully tested, it has been tested under GPU and python3. But in theory there shouldn't be too many problems on python2 and CPU. The basic part (the first five chapters) explains the content of PyTorch. This part introduces the main modules in PyTorch and some tools commonly used in deep learning. For this part of the content, Jupyter Notebook is used as a teaching tool here, and readers can modify and run with notebooks and repeat experiments. -
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node2vec
Learn continuous vector embeddings for nodes in a graph using biased R
...It allows researchers and practitioners to apply node2vec to various graph datasets and evaluate embedding quality on downstream tasks. By bridging ideas from graph theory and word embedding models, this project demonstrates how graph-based machine learning can be made efficient and flexible. -
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XInt
A big number C++ class
XInt is a big number class that is designed to be a "drop-in" replacement for both integers and all floating point types. In theory, you could take an existing programme, replace all integer and floating point types with XInt and it should compile and run exactly as it did before but with greater precision. There are some exceptions. Obviously, if you are relying on over/underflow behaviour, it won't work, XInt does not overflow! It will underflow but in a controlled fashion and you can specify how it behaves. ...
