Search Results for "/storage/emulated/0/android/data/net.sourceforge.uiq3.fx603p/files"
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Showing 178 open source projects for "/storage/emulated/0/android/data/net.sourceforge.uiq3.fx603p/files"
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1
The Data Engineering Handbook
Links to everything you'd ever want to learn about data engineering
The Data Engineering Handbook is a comprehensive, community-curated repository that aggregates essential learning resources for anyone interested in becoming a professional data engineer. Rather than being a code project itself, it’s a learning handbook that links to books, articles, tutorials, community groups, boot camps, and real-world project examples that collectively form a roadmap to mastering data engineering skills. -
2
Book2_Beauty-of-Data-Visualization
Machine Learning, Criticism and Correction
Book2_Beauty-of-Data-Visualization is an open educational project that teaches the principles and techniques of effective data visualization using Python and modern plotting libraries. The repository focuses on both the technical and aesthetic aspects of visual analytics, helping learners understand how to communicate data clearly and persuasively. It includes practical examples that demonstrate how different chart types reveal patterns, trends, and distributions in real datasets. ... -
3
Python Zero to Hero for DevOps Engineers
Learn Python from DevOps Engineer point of you
Python Zero to Hero for DevOps Engineers is a structured “Python Zero to Hero for DevOps Engineers” course laid out as a day-by-day learning path. The repository is organized into Day-01 through Day-19 folders plus a small sample app, which makes it very easy to follow in sequence like a bootcamp. The curriculum starts with Python installation, environment setup, and writing your first script, then quickly moves into data types, strings, regular expressions, variables, and functions. ... -
4
The Grand Complete Data Science Guide
Data Science Guide With Videos And Materials
The Grand Complete Data Science Materials is a repository curated by a data-science educator that aggregates a wide range of learning resources — from basic programming and math foundation to advanced topics in machine learning, deep learning, natural language processing, computer vision, and deployment practices — into a structured, centralized collection aimed at learners seeking a comprehensive path to data science mastery. -
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5
Hello Python
Comprehensive tutorial repository aimed at teaching the Python program
...It includes over 100 classes and about 44 hours of video instruction, combined with code samples, projects, and a chat community for support. The material covers the fundamentals—variables, data types, loops, functions—as well as intermediate topics like date handling, list comprehensions, file IO, regular expressions, modules, and packages. The course is designed to be accessible: no prior programming experience required, and the resources are freely available. In addition, it is accompanied by a practical coding approach (projects) and is maintained as an open-source repository under Apache-2.0 license. ... -
6
ktrain
ktrain is a Python library that makes deep learning AI more accessible
ktrain is a Python library that makes deep learning and AI more accessible and easier to apply. ktrain is a lightweight wrapper for the deep learning library TensorFlow Keras (and other libraries) to help build, train, and deploy neural networks and other machine learning models. Inspired by ML framework extensions like fastai and ludwig, ktrain is designed to make deep learning and AI more accessible and easier to apply for both newcomers and experienced practitioners. With only a few lines... -
7
Recommenders
Best practices on recommendation systems
...Several utilities are provided in reco_utils to support common tasks such as loading datasets in the format expected by different algorithms, evaluating model outputs, and splitting training/test data. Implementations of several state-of-the-art algorithms are included for self-study and customization in your own applications. Please see the setup guide for more details on setting up your machine locally, on a data science virtual machine (DSVM) or on Azure Databricks. Independent or incubating algorithms and utilities are candidates for the contrib folder. ... -
8
Think Python 2
LaTeX source and supporting code for Think Python, 2nd edition
ThinkPython2 is the repository for the second edition of Allen Downey’s Think Python textbook, which teaches programming fundamentals in Python to beginners. The code includes all of the example programs, exercises, and supplementary files referenced in the book, allowing learners to run the examples, experiment, and extend them. The repository contains clean, well-commented Python scripts that are easy to follow and map directly to chapters of the text, covering topics like variables, control flow, functions, recursion, data structures (lists, dictionaries), classes and objects, file I/O, and algorithmic thinking. ... -
9
PythonPark
Python open source project "The Road to Self-Study Programming"
...For someone self-teaching Python (or transitioning into coding/data science), the repository presents a one-stop “home base” of content, saving them from hunting scattered tutorials across the internet. -
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10
Ajenti 2
Ajenti Core and stock plugins
...Ajenti 2 can be easily installed with pip and the provided script. Picks up your current configuration and works on your existing system as-is, without any preparation. Does not overwrite your config files, options and comments. All changes are non-destructive. Includes lots of plugins for system and software configuration, monitoring and management. Ajenti 2 is easily extensible using Python. Plugin development is quick and pleasant with Ajenti APIs. Write your first plugin. Pleasant to look at, satisfying to click and accessible anywhere from tablets and mobile. ... -
11
Interpretable machine learning
Book about interpretable machine learning
...As the programmer of an algorithm you want to know whether you can trust the learned model. Did it learn generalizable features? Or are there some odd artifacts in the training data which the algorithm picked up? This book will give an overview over techniques that can be used to make black boxes as transparent as possible and explain decisions. In the first chapter algorithms that produce simple, interpretable models are introduced together with instructions how to interpret the output. The later chapters focus on analyzing complex models and their decisions. ... -
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Book5_Essentials-Probability-Statistics
The book 5 of statistics in simplicity
...Like the other books in the series, it blends mathematical explanation with Python-based experimentation. Overall, the project provides a practical statistical foundation for students advancing into AI and data science. -
13
Book1_Python-For-Beginners
The Iris Book: Addition, Subtraction, Multiplication, and Division
...It integrates visual aids and annotated code examples to help learners understand not just how Python works but why certain patterns are used. The material is structured to support self-paced learning, making it suitable for students, career switchers, and hobbyists. Because the book is part of a larger data science pathway, it also prepares readers for later work in visualization and machine learning. Overall, it serves as an accessible on-ramp into Python within a broader analytical learning journey. -
14
Book3_Elements-of-Mathematics
From Addition, Subtraction, Multiplication, and Division to ML
Book3_Elements-of-Mathematics is an open learning resource in the Visualize-ML collection that introduces core mathematical foundations required for modern data science and AI. The repository presents topics such as algebra, calculus fundamentals, and mathematical reasoning using a highly visual and beginner-friendly approach. Its goal is to reduce the intimidation barrier often associated with formal mathematics by combining diagrams, structured explanations, and applied examples. The content is organized progressively so learners can build confidence before moving into more advanced quantitative subjects. ... -
15
Ansible Automation Platform Workshops
Training course for Ansible automation platform
...The demos are intended for effectively demonstrating Ansible capabilities with prescriptive guides on the Ansible Automation Workshop infrastructure. Check out the optional website which is rendered automatically from markdown files using Github Pages. -
16
nanoGPT
The simplest, fastest repository for training/finetuning models
NanoGPT is a minimalistic yet powerful reimplementation of GPT-style transformers created by Andrej Karpathy for educational and research use. It distills the GPT architecture into a few hundred lines of Python code, making it far easier to understand than large, production-scale implementations. The repo is organized with a training pipeline (dataset preprocessing, model definition, optimizer, training loop) and inference script so you can train a small GPT on text datasets like Shakespeare... -
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Scientific Visualization
An open access book on scientific visualization using python
The Scientific Visualization book is a freely available open-access textbook that introduces how to produce effective scientific visualizations using Python, focusing especially on leveraging the popular plotting library Matplotlib (and related tools). It goes beyond simple plotting tutorials and emphasizes design principles: how to choose colors, layout subplots, annotate graphs, and present data in a way that is both accurate and visually compelling. As such, it serves as a guide for researchers, data scientists, and academic authors who need to create publication-quality figures or explanatory graphics, rather than quick exploratory plots. It includes extensive examples that demonstrate best practices — for instance handling multiple subplots, combining line plots with scatter/density overlays, or rendering high-resolution vector graphics for print. -
18
PocketFlow Tutorial Codebase Knowledge
Codebase to Tutorial
...The repository builds on a lightweight 100-line LLM framework and uses natural language models to inspect repository structures, identify core abstractions, map dependencies, and articulate the reasoning behind code design and interactions. By crawling code files, extracting higher-level patterns, and using large language models to narrate explanations, the system aims to help developers — especially those new to a codebase — understand unfamiliar projects without manual deep reading. It supports both GitHub URL crawling and local directory analysis, and can tailor output tutorials to different languages, making it accessible for international developers. -
19
Stanza
Stanford NLP Python library for many human languages
...The toolkit is designed to be parallel among more than 70 languages, using the Universal Dependencies formalism. Stanza is built with highly accurate neural network components that also enable efficient training and evaluation with your own annotated data. -
20
Megatron-LM
Ongoing research training transformer models at scale
...The repository provides both a reference training implementation and Megatron Core, a composable library of high-performance building blocks for custom large-model pipelines. It supports advanced parallelism strategies including tensor, pipeline, data, expert, and context parallelism, enabling training across massive multi-GPU and multi-node clusters. The framework includes mixed-precision training options such as FP16, BF16, FP8, and FP4 to maximize performance and memory efficiency on modern hardware. Megatron-LM is widely used in research and industry for pretraining GPT-, BERT-, T5-, and multimodal-style models, with tooling for checkpoint conversion and interoperability with Hugging Face. ... -
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Book4_Power-of-Matrix
Book_4_Matrix Power | The Iris Book: From Addition, Subtraction
...The repository is continuously updated and intended to accompany the broader Visualize-ML learning ecosystem. Overall, it serves as a visually driven mathematical foundation for students preparing for data science and machine learning work. -
22
AI Researcher
An autonomous AI researcher
...Each agent operates with clear roles — such as researcher, analyst, and summarizer — and they communicate through a task-management interface that ensures progress tracking and iterative refinement. The system emphasizes modularity, so teams can swap in new reasoning modules, data retrieval strategies, or domain knowledge bases depending on the research topic. Through self-supervised feedback loops, agents adjust their strategies based on prior outcomes, improving both the quality and relevance of results over time. -
23
Hello SQL
Spanish-language course repository that teaches fundamentals of SQL
...The materials emphasize real-world query writing, schema design basics, and the mental model behind SELECT, JOIN, GROUP BY, and subqueries. Learners progress from setup and connection to hands-on exercises that build confidence with CRUD operations and data modeling. The repository’s structure favors incremental learning, with clear folders, references, and exercises you can run locally. It targets absolute beginners as well as developers from other stacks who want a clean, project-based path into SQL. -
24
eduActiv8
A collection of interactive educational exercises
...The latest version has been partially redesigned to improve usability, but certain activities will still be in the somewhat "prototype quality" - this will be gradually redesigned as time allows. Currently, it is available for Windows, MacOS, Linux and Android (you may need to allow unknown sources to install it on Android). Packages for multiple Linux distributions are available from: https://software.opensuse.org//download.html?project=home%3Aimiolek-i&package=eduactiv8 Support eduActiv8 at https://ko-fi.com/eduactiv8 -
25
Prime number ( primenumbers )
Benchmark for 50 000 000 prime numbers as single and multicore
Simple source files and compiled JAR Java programs, for benchmark 50 000 000 cycle finding prime numbers. On Intel(R) Core(TM) i5-8600K CPU, Windows 10 20H2, i have 39 second on single core and 7,6 second on multi core. (PS: C++ multicore 6 second). Added C files for gcc compiler in Windows 10 and for Xcode C command line project in MacOS ( tested on Mac mini M2 with single core 16 to 25 sec and multicore 2,3 to 5 second by compiler -O switch).
