Search Results for "code coverage" - Page 5
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163 projects for "code coverage" with 1 filter applied:
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Data Science Notes
Curated collection of data science learning materials
Data Science Notes is a large, curated collection of data science learning materials, with explanations, code snippets, and structured notes across the typical end-to-end workflow. It spans foundational math and statistics through data wrangling, visualization, machine learning, and practical project organization. The content emphasizes hands-on understanding by pairing narrative notes with runnable examples, making it useful for both self-study and classroom settings. Because it aggregates... -
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CyC2018.github.io
Personal knowledge site built with GitHub Pages
This is a personal knowledge site built with GitHub Pages, organizing computer science notes and study materials in a browsable format. It aggregates content across algorithms, data structures, networking, operating systems, databases, and interview preparation into a coherent index. The presentation emphasizes succinct explanations paired with diagrams, tables, or code snippets to speed recall. Because the site is generated from a repository, it benefits from issue tracking, pull requests,... -
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SentEval
A python tool for evaluating the quality of sentence embeddings
SentEval is a standardized toolkit for evaluating sentence embeddings across a wide spectrum of downstream tasks and probing tests. It defines a simple interface—provide an encoder function from sentences to vectors—and then runs consistent training/evaluation loops for tasks like sentiment, entailment, paraphrase, and semantic textual similarity. The suite also contains linguistic probing tasks that illuminate what properties embeddings capture, such as tense, word order, or syntactic... -
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American Fuzzy Lop
American fuzzy lop - a security-oriented fuzzer
AFL (American Fuzzy Lop) is a widely used graybox fuzzer that discovers bugs by mutating inputs and steering execution using lightweight instrumentation. Instead of random mutations alone, it uses coverage feedback to evolve input corpora, pushing programs into deeper and more interesting code paths. Its workflow emphasizes quick start: point it at a target binary with compile-time instrumentation (or use QEMU-based mode when recompilation isn’t possible), seed it with a small corpus, and let it iterate. AFL is known for finding serious security issues in complex software due to its corpus minimization, queue management, and deterministic mutation stages that balance breadth and depth. ... -
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pytorch-tutorial
PyTorch Tutorial for Deep Learning Researchers
pytorch-tutorial is a highly popular educational repository that teaches deep learning with PyTorch through step-by-step examples and well-structured lessons. It is designed primarily for beginners and intermediate practitioners who want to understand PyTorch fundamentals and quickly move toward building real neural network models. The repository walks users through core concepts such as tensors, autograd, neural network modules, convolutional networks, recurrent networks, and transfer... -
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Tensorflow and deep learning
A crash course in six episodes for software developers
Tensorflow and deep learning repository is an educational deep learning crash course designed to help software developers quickly understand and apply machine learning concepts without requiring advanced academic background. It is structured as a series of guided lessons that combine theoretical explanations, practical examples, and runnable code, allowing learners to build intuition while actively experimenting with models. The repository covers core neural network concepts such as weights,... -
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JS Bits
JavaScript concepts with code
...The repository is organized by topic so that learners can jump directly into areas they want to reinforce or explore, and community contributions are welcomed to expand coverage or add alternative explanations. Rather than being a formal textbook, js-bits acts like a developer’s code-centric cheat sheet that helps bridge the gap between abstract language specifications and real usage patterns in the browser or Node.js. -
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threadandjuc
High performance three-high-import import system
threadandjuc is a Java learning repository focused on multithreading, concurrency, and JUC concepts. It is designed to help developers understand how Java concurrent programming works through examples, explanations, and practical project-style demonstrations. The project covers topics such as threads, locks, synchronization, thread pools, concurrent collections, and high-performance data handling. It is especially useful for Java developers preparing for interviews or improving their... -
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XcodeCoverage
Code coverage for Xcode projects (Objective-C only)
XcodeCoverage is a tool that automates the generation of code coverage reports for iOS and macOS projects. It helps developers measure how much of their code is tested, facilitating better testing practices and coverage improvement. -
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Swift tips & tricks
A collection of Swift tips & tricks that I've shared on Twitter
Swift tips & tricks is a curated collection of practical Swift programming tips and tricks compiled from real-world usage and shared insights, primarily originating from short-form content like social media posts. The repository serves as a knowledge base of concise, focused techniques that demonstrate idiomatic Swift patterns, language features, and problem-solving approaches. Each tip highlights a specific concept, such as functional programming techniques, protocol specialization, error... -
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Node.JS Course
Materials, slides and other goodies from my Node.JS courses
This repository accompanies a hands-on Node.js course that builds production-minded skills through projects and exercises rather than slideware. It walks learners from fundamentals to pragmatic topics such as configuration management, process models, database integration, caching, and message queues. Security practices—input validation, secrets handling, and least-privilege IAM—are woven into examples instead of tacked on at the end. The curriculum also covers operational concerns like... -
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Deep-Learning-with-PyTorch-Tutorials
Deep Learning and PyTorch Introduction Video Tutorial with Source Code
Deep-Learning-with-PyTorch-Tutorials is a companion repository for an introductory deep learning course built around PyTorch. It provides source code, notebooks, and presentation materials for a practical video-based learning path. The lessons begin with PyTorch setup, tensors, indexing, mathematical operations, gradients, and basic optimization. They then move into neural networks, logistic regression, multilayer perceptrons, CNNs, ResNet, RNNs, LSTMs, autoencoders, VAEs, GANs, graph... -
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Machine Learning From Scratch
Bare bones NumPy implementations of machine learning models
ML-From-Scratch is an open-source machine learning project that demonstrates how to implement common machine learning algorithms using only basic Python and NumPy rather than relying on high-level frameworks. The goal of the project is to help learners understand how machine learning algorithms work internally by building them step by step from fundamental mathematical operations. The repository includes implementations of algorithms ranging from simple models such as linear regression and... -
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Java Tutorial For Beginners
Java Tutorial For Beginners with 500 Code Examples
Java Tutorial for Beginners is a structured educational repository designed to introduce fundamental Java programming concepts through practical examples and guided exercises. It walks learners step by step through core topics such as variables, control structures, object-oriented programming, and basic application development. The content is organized to build understanding progressively, making it accessible for those with little or no prior programming experience. It emphasizes hands-on... -
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Didact
A DIY guide to build your own React
Didact is an educational JavaScript project that teaches developers how to build a small React-like library from scratch. It accompanies a series of explanatory posts that break down React concepts step by step. The project covers rendering DOM elements, element creation, JSX, virtual DOM behavior, reconciliation, components, state, Fiber-style incremental reconciliation, and hooks. Its goal is not to replace React, but to make React’s internal ideas easier to understand through a compact... -
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pytorch-examples
Simple examples to introduce PyTorch
The pytorch-examples project is a collection of concise and practical examples demonstrating how to use PyTorch for machine learning and deep learning tasks. It focuses on clarity and minimalism, providing small, self-contained scripts that illustrate key concepts such as neural network training, optimization, and data handling. The examples cover a range of topics including supervised learning, generative models, and reinforcement learning, making it a valuable resource for both beginners... -
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PythonRobotics
Python sample codes and textbook for robotics algorithms
PythonRobotics is a Python code collection and textbook for learning robotics algorithms through readable examples. It covers practical topics such as localization, mapping, path planning, path tracking, control, SLAM, and autonomous navigation. The project is written to make each algorithm’s core idea easy to understand, rather than hiding the logic behind large frameworks. It keeps dependencies minimal so learners can focus on the math, implementation, and behavior of each robotics method.... -
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spark-ml-source-analysis
Spark ml algorithm principle analysis and specific source code
spark-ml-source-analysis is a technical repository that analyzes the internal implementation of machine learning algorithms within Apache Spark’s MLlib library. The project aims to help developers and data scientists understand how distributed machine learning algorithms are implemented and optimized inside the Spark ecosystem. Instead of providing a runnable software system, the repository focuses on explaining algorithm principles and examining the underlying source code used in Spark’s... -
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Text Typesetting
Concise guide to better typography and formatting practices
Text-Typesetting is a concise guide to better typography and formatting practices for technical writing. It collects rules of spacing, punctuation, emphasis, and layout that improve readability, especially in documents mixing multiple languages or code. The repository focuses on consistent micro-typography—such as when to use non-breaking spaces, how to place punctuation around quotes, and how to format lists and headings. Examples show the before/after impact of small adjustments, making... -
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MIT Deep Learning
Tutorials, assignments, and competitions for MIT Deep Learning
MIT Deep Learning is an open-source repository that contains tutorials, assignments, and learning materials related to deep learning courses taught at MIT. The repository provides hands-on tutorials that introduce the fundamental concepts behind neural networks, deep learning architectures, and modern machine learning techniques. Many of the tutorials include practical implementations that demonstrate tasks such as image classification, generative models, and neural network training... -
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data-science-ipython-notebooks
Data science Python notebooks: Deep learning
Data Science IPython Notebooks is a broad, curated set of Jupyter notebooks covering Python, data wrangling, visualization, machine learning, deep learning, and big data tools. It aims to be a practical map of the ecosystem, showing hands-on examples with libraries such as NumPy, pandas, matplotlib, scikit-learn, and others. Many notebooks introduce concepts step by step, then apply them to real datasets so readers can see techniques in action. Advanced sections touch on neural networks and... -
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vim-go-tutorial
Tutorial for vim-go
vim-go-tutorial is a step-by-step educational repository that teaches developers how to install, configure, and effectively use the vim-go plugin for Go development inside Vim. The project walks users through a structured learning path that begins with quick setup and progresses through building, testing, refactoring, navigation, and code generation workflows. It is designed primarily for developers who want to turn Vim into a full-featured Go IDE while understanding the tooling rather than... -
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N-blog
Express + MongoDB
N-blog is a Chinese-language Node.js course built around creating a multiuser blogging application with Express and MongoDB. It teaches the surrounding platform before guiding readers through the complete example project. Early chapters cover Node.js installation, module exports, promises, environment variables, package metadata, npm, routing, templates, middleware, and error handling. The application section adds configuration, database access, sessions, flash messages, permissions,... -
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Compare GAN
Compare GAN code
compare_gan is a research codebase that standardizes how Generative Adversarial Networks are trained and evaluated so results are comparable across papers and datasets. It offers reference implementations for popular GAN architectures and losses, plus a consistent training harness to remove confounding differences in optimization or preprocessing. The library’s evaluation suite includes widely used metrics and diagnostics that quantify sample quality, diversity, and mode coverage. With... -
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Learn_Machine_Learning_in_3_Months
This is the code for "Learn Machine Learning in 3 Months"
This repository outlines an ambitious self-study curriculum for learning machine learning in roughly three months, emphasizing breadth, momentum, and hands-on practice. It sequences core topics—math foundations, classic ML, deep learning, and applied projects—so learners can pace themselves week by week. The plan mixes reading, lectures, coding assignments, and small build-it-yourself projects to reinforce understanding through repetition and implementation. Because ML is a wide field, the...
