Search Results for "patterns" - Page 9
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Showing 295 open source projects for "patterns"
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data-science-on-gcp
Source code accompanying book: Data Science on the GCP
The data-science-on-gcp repository is a comprehensive collection of code examples and end-to-end workflows that accompany the book Data Science on the Google Cloud Platform, designed to teach developers how to build scalable data science and machine learning systems using Google Cloud services. It provides structured, chapter-aligned implementations that guide users through the full lifecycle of a data science project, including data ingestion, storage, processing, analysis, model training,... -
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FairScale
PyTorch extensions for high performance and large scale training
...Its components are modular, so teams can adopt just the sharding optimizer or the pipeline engine without rewriting their training loop. FairScale puts emphasis on correctness and debuggability, offering hook points, logging, and reference examples for common trainer patterns. Although many ideas have since landed in core PyTorch, FairScale remains a valuable reference and a practical toolbox for squeezing more performance out of multi-GPU and multi-node jobs. -
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dein.vim
Dark powered Vim/Neovim plugin manager
...It supports managing plugins from GitHub and local directories, with a design that balances speed (like vim-plug) and flexibility (like NeoBundle). Active development has ceased, with only bug fixes expected going forward. Function API and familiar patterns, without commands or dependency hell. Clean asynchronous installation supported. Supports plugins from local or remote sources, and also Non-Github plugins. -
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makemore
An autoregressive character-level language model for making more
makemore is a compact educational framework for training character-level language models on line-separated text datasets. Given examples such as personal names, company names, or dictionary words, it learns their statistical structure and generates new examples with similar patterns. The project intentionally keeps most functionality in one hackable Python file and requires only PyTorch. It includes implementations ranging from simple bigram models to MLPs, recurrent networks, LSTMs, GRUs, and transformers. Training progress, checkpoints, logs, and generated samples are written to a selected working directory. ... -
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LSTMs for Human Activity Recognition
Human Activity Recognition example using TensorFlow on smartphone
...The project uses the well-known Human Activity Recognition dataset derived from smartphone accelerometer and gyroscope signals. Through the use of sequential neural network architectures, the system learns patterns in motion data that correspond to activities such as walking, sitting, standing, or climbing stairs. The repository includes data preprocessing scripts, neural network architecture definitions, and training pipelines that allow researchers to reproduce and modify the experiments. It serves as an educational example of how deep learning models can process temporal sensor signals for pattern recognition tasks. -
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DialoGPT
Large-scale pretraining for dialogue
...The system is built on the GPT-2 architecture and is designed specifically for multi-turn conversation tasks, enabling machines to produce coherent responses during interactive dialogue. The model was trained on a massive dataset of approximately 147 million conversational exchanges extracted from Reddit discussion threads, allowing it to learn patterns of natural human conversation. DialoGPT provides multiple pretrained model sizes and includes code for training, fine-tuning, and evaluating dialogue generation models. The repository also contains scripts for preparing conversation datasets and reproducing experimental benchmarks related to conversational AI research. -
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grab-site
Web crawler for archiving and backing up sites into WARC archives
...Internally, the crawler uses a fork of the wpull engine to fetch and process web pages efficiently during large-scale crawls. grab-site includes a built-in dashboard that displays real-time crawl activity, including which URLs are currently being processed and how many remain in the queue. Users can dynamically apply ignore patterns during an active crawl, allowing them to skip problematic or unnecessary URLs that could slow down or block the archiving process. grab-site also provides predefined ignore sets for common site structures such as forums and other complex web platforms. Additional mechanisms like duplicate page detection help avoid re-crawling identical content. -
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ety
A Python module to discover the etymology of words
...The project relies on structured datasets that map relationships between words and languages, enabling systematic exploration of linguistic evolution. It is particularly useful for developers, linguists, and researchers interested in historical language patterns and word origins. -
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mlscraper
ML-based HTML scraper that learns extraction rules from examples
...Instead of defining extraction logic by hand, users provide a few examples of the data they want to retrieve from a webpage. It analyzes those examples within the HTML document and determines patterns or rules that can be used to extract the same type of information from similar pages. Once trained, the generated scraper can process new pages and return the extracted data in structured formats such as dictionaries or lists. This approach simplifies web scraping tasks by shifting the focus from rule-writing to example-based training. ... -
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Google Cloud Vision API examples
Sample code for Google Cloud Vision
...Although the repository has been marked as deprecated in favor of language-specific repositories for new work, it still serves as a broad reference hub for legacy examples and multi-language implementation patterns. -
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Tile Pattern Exporter
Tile large format PNG patterns into print-at-home PDF pages
You can tile large format PNG patterns into print-at-home PDF pages. Created for LearnMYOG. This set of scripts automates the tiling of large format PNG files into letter(A4), tabloid(A3), and A0 sized PDF pages with print margins, alignment and cut guides, page numbers, and a copyright stamp to each page. For best results, input an exported PNG with size in multiples of 7.5 inches wide and 10 inches tall @ 300dpi. -
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Code Cookbook
Reusable code patterns which you can use as reference or copy
Reusable code patterns which you can use as reference or copy to your project. Achieve small or large tasks using recipes that contain steps, scripts, and config files. -
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pyWhat
Identify emails, IP addresses, and more
...Given inputs such as hex strings, URLs, email addresses, IP addresses, credit card numbers, cryptocurrency wallets, or entire .pcap capture files, it scans for structured patterns and tells you what it finds. The tool is recursive: it can traverse files and directories to extract meaningful entities, which is useful when analyzing malware samples, network captures, or code repositories at scale. It offers powerful filters called “tags” and distributions that let you narrow results to specific categories like bug bounties, cryptocurrencies, or AWS-related artifacts. ... -
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Pandas TA
Python 3 Pandas Extension with 130+ Indicators
...Pandas Technical Analysis (Pandas TA) is an easy-to-use library that leverages the Pandas package with more than 130 Indicators and Utility functions and more than 60 TA Lib Candlestick Patterns. Many commonly used indicators are included, such as: Candle Pattern(cdl_pattern), Simple Moving Average (sma) Moving Average Convergence Divergence (macd), Hull Exponential Moving Average (hma), Bollinger Bands (bbands), On-Balance Volume (obv), Aroon & Aroon Oscillator (aroon), Squeeze (squeeze) and many more. -
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Practice Python
Part of my daily plan for studying Python
...The tasks emphasize real coding over passive reading, nudging you to write, run, and iterate on solutions. Exercises commonly target strings, lists, dictionaries, control flow, functions, classes, and common algorithms, reinforcing idiomatic Python patterns. Many problems are intentionally minimal in boilerplate so you can concentrate on logic and clarity. The collection is well suited to daily practice sessions or warm-ups before tackling more complex projects. It is also friendly for learners returning to Python after time away, helping reacquire muscle memory through repetition. -
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RegexAssistant
Regex Windows GUI app to help learn, create,& test Regular Expressions
RegexAssistant is a Regex GUI application to help learn, create, and test Regular Expressions. It's an open source stand alone Windows application. RegexAssistant is great for beginners and intermediate-advanced regex users. -It helps beginners to learn regex by providing examples and token cheat-sheet. -Intermediate-advanced users can use RegexAssistant to test complex expressions. -
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Hands-on Unsupervised Learning
Code for Hands-on Unsupervised Learning Using Python (O'Reilly Media)
...Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied; this is where unsupervised learning comes in. Unsupervised learning can be applied to unlabeled datasets to discover meaningful patterns buried deep in the data, patterns that may be near impossible for humans to uncover. Author Ankur Patel provides practical knowledge on how to apply unsupervised learning using two simple, production-ready Python frameworks - scikit-learn and TensorFlow. With the hands-on examples and code provided, you will identify difficult-to-find patterns in data. -
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MLOps Course
Learn how to design, develop, deploy and iterate on ML apps
The MLOps Course by Goku Mohandas is an open-source curriculum that teaches how to combine machine learning with solid software engineering to build production-grade ML applications. It is structured around the full lifecycle: data pipelines, modeling, experiment tracking, deployment, testing, monitoring, and iteration. The repository itself contains configuration, code examples, and links to accompanying lessons hosted on the Made With ML site, which provide detailed narrative explanations... -
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Awesome Community Detection Research
A curated list of community detection research papers
A collection of community detection papers. A curated list of community detection research papers with implementations. Similar collections about graph classification, classification/regression tree, fraud detection, and gradient boosting papers with implementations. -
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ngxtop
Real-time metrics for nginx server
...It automatically detects the access log location and format in many cases, enabling quick deployment with minimal configuration. Users can also customize queries, grouping logic, and filters to analyze specific traffic patterns or metrics. By combining the familiarity of the UNIX top paradigm with flexible log parsing, ngxtop offers a lightweight yet powerful approach to real-time web server observability. -
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BeaEngine 5
BeaEngine disasm project
BeaEngine is a C library designed to decode instructions from 16-bit, 32-bit and 64-bit intel architectures. It includes standard instructions set and instructions set from FPU, MMX, SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2, VMX, CLMUL, AES, MPX, AVX, AVX2, AVX512 (VEX & EVEX prefixes), CET, BMI1, BMI2, SGX, UINTR, KL, TDX and AMX extensions. If you want to analyze malicious codes and more generally obfuscated codes, BeaEngine sends back a complex structure that describes precisely the... -
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Phishing Catcher
Real-time phishing domain detection via Certificate Transparency logs
...Each certificate often contains one or more domain names, which the tool analyzes to determine whether they resemble suspicious or phishing-related domains. phishing_catcher applies a configurable scoring mechanism that assigns numeric values to certain keywords, patterns, or top-level domains found within certificate domain names. When a domain’s score exceeds predefined thresholds, it is flagged as potentially malicious and reported accordingly. It operates continuously, processing certificate updates as they arrive and displaying or logging domains that appear suspicious. This approach allows analysts, researchers, and security teams to identify phishing infrastructure early. -
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surpriver
Find big moving stocks before they move using machine learning
...The system analyzes historical stock price and volume data to detect anomalies that could indicate potential trading opportunities. By applying machine learning techniques to market indicators, the tool attempts to identify patterns in trading behavior that deviate significantly from normal market activity. These anomalies are interpreted as signals that a stock may soon experience a major upward or downward move. The framework includes modules for retrieving market data, computing technical indicators, and applying anomaly detection algorithms to identify unusual patterns. -
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Eiten
Statistical and Algorithmic Investing Strategies for Everyone
Eiten is an open-source Python project focused on providing statistical and algorithmic trading strategies powered by data analysis and machine learning techniques. It is designed to make quantitative investing more accessible by offering ready-to-use strategies that analyze market behavior, detect patterns, and generate actionable insights. The project includes tools for evaluating stock performance, identifying trends, and applying algorithmic models to financial data, enabling users to experiment with different investment approaches. It is part of the broader Tradytics ecosystem, which emphasizes AI-driven financial tools for identifying opportunities in the stock market. ... -
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Sparse Attention
"Generating Long Sequences with Sparse Transformers" examples
Sparse Attention is OpenAI’s code release for the Sparse Transformer model, introduced in the paper Generating Long Sequences with Sparse Transformers. It explores how modifying the self-attention mechanism with sparse patterns can reduce the quadratic scaling of standard transformers, making it possible to model much longer sequences efficiently. The repository provides implementations of sparse attention layers, training code, and evaluation scripts for benchmark datasets. It highlights both fixed and learnable sparsity patterns that trade off computational cost and model expressiveness. ...
