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Back to internlm/tutorial

Open-source alternatives to Tutorial

30 open-source projects similar to internlm/tutorial, ranked by how many features they have in common. Compare stars, activity and what each one does to find the best Tutorial alternative.

  • adambard/learnxinyminutes-docsadambard avatar

    adambard/learnxinyminutes-docs

    12,287View on GitHub↗

    This project is a collection of programming language references and syntax cheat sheets designed for rapid developer onboarding. It serves as a library of code-based documentation that uses valid source code files to provide whirlwind tours of various language specifications. The project focuses on programming language learning by providing concise, commented code examples that explain core features and syntax in place. This approach enables developers to quickly grasp language-specific patterns, data types, and execution flow through a consistent reference format. The content covers a broad

    Markdown
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  • adrianmoisey/learn-pythonadrianmoisey avatar

    adrianmoisey/learn-python

    240View on GitHub↗

    A collection of links that teach python

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  • ageron/handson-mlageron avatar

    ageron/handson-ml

    25,608View on GitHub↗

    This is a machine learning educational repository consisting of a collection of notebooks and code examples. It provides practical implementations of diverse machine learning algorithms and workflows, ranging from traditional scientific computing to deep learning. The project features specific implementations of Scikit-Learn models, such as decision trees, random forests, and support vector machines, as well as TensorFlow examples for building neural networks, convolutional layers, and recurrent architectures. It also includes tutorials on reinforcement learning development and the creation o

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    View on GitHub↗25,608
  • alex/what-happens-whenalex avatar

    alex/what-happens-when

    43,189View on GitHub↗

    This project is a collaborative knowledge base and technical learning resource that provides a detailed breakdown of the internal processes occurring within modern computing environments. It serves as a comprehensive educational reference, tracing the step-by-step operations triggered by common user interactions and network requests to explain how hardware and software components interact across the entire stack. The guide distinguishes itself by offering deep technical insights into the journey from physical input to visual output. It covers the low-level mechanics of hardware interrupt hand

    View on GitHub↗43,189

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  • anderson-joyle/blockchain-for-humansA

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    andrewharmellaw/wardley-maps-book

    306View on GitHub↗

    This is an Asciidoc book of Simon Wardley's "Wardley Maps". It simply takes all his medium posts and joins them together for ease of reading. The intention is to be entirely faithful to the original posts - I've not even fixed the few spelling mistakes - while allowing various output versions to…

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  • andrewt3000/dl4nlpandrewt3000 avatar

    andrewt3000/DL4NLP

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    State of the art resources for NLP sequence modeling tasks such as machine translation, image captioning, and dialog.

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  • anotiawang/open-assistant-helperAnotiaWang avatar

    AnotiaWang/open-assistant-helper

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    English | 简体中文

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  • antonio-morales/fuzzing101antonio-morales avatar

    antonio-morales/Fuzzing101

    3,796View on GitHub↗

    Fuzzing101 is an educational resource providing a structured curriculum and containerized security labs for learning software fuzzing and vulnerability research. It functions as a training course that guides users through the process of identifying security flaws using systematic input manipulation and memory corruption analysis. The project distinguishes itself by providing isolated environments that ensure consistent build dependencies for practicing software instrumentation and crash triaging. It includes a practical tutorial on using evolutionary fuzzing engines and instrumentation tools

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  • aphyr/distsys-classaphyr avatar

    aphyr/distsys-class

    9,717View on GitHub↗

    This project provides educational materials and courseware focused on the theoretical and practical foundations of distributed systems design. It serves as a comprehensive curriculum covering the disciplines of consensus, data consistency, reliability engineering, and scalability. The instructional content focuses on achieving cluster agreement through consensus algorithms and managing system-wide state via coordination frameworks. It includes a dedicated guide to data theory, exploring replication strategies, consistency models, and data convergence. The courseware covers a broad capability

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  • arbox/machine-learning-with-rubyarbox avatar

    arbox/machine-learning-with-ruby

    2,215View on GitHub↗

    Curated list: Resources for machine learning in Ruby

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    View on GitHub↗2,215
  • arnas/awesome-pytorch-scholarshipA

    arnas/awesome-pytorch-scholarship

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  • aronwalsh/mlformaterialsaronwalsh avatar

    aronwalsh/MLforMaterials

    151View on GitHub↗

    Online resource of a practical machine learning course in the Department of Materials at Imperial College London.

    Jupyter Notebook
    View on GitHub↗151
  • artem-zinnatullin/thecontext-podcastartem-zinnatullin avatar

    artem-zinnatullin/TheContext-Podcast

    622View on GitHub↗

    Podcast about Android Development with Hannes Dorfmann, Artem Zinnatullin, Artur Dryomov and wonderful guests!

    View on GitHub↗622
  • ashishpatel26/andrew-ng-notesashishpatel26 avatar

    ashishpatel26/Andrew-NG-Notes

    3,594View on GitHub↗

    This project is a collection of structured study notes and notebooks serving as an educational resource for deep learning and neural network fundamentals. It provides a technical reference for implementing machine learning theory, covering everything from basic network design to the construction of advanced architectures. The material specifically focuses on the implementation of convolutional neural networks for computer vision and sequence models for natural language processing. It includes detailed guidance on building object detection systems, face recognition, and speech transcription mo

    Jupyter Notebookandrew-ngandrew-ng-courseandrew-ng-machine-learning
    View on GitHub↗3,594
  • ashishpatel26/real-time-ml-projectashishpatel26 avatar

    ashishpatel26/Real-time-ML-Project

    762View on GitHub↗

    A curated list of applied machine learning and data science notebooks and libraries across different industries.

    View on GitHub↗762
  • awesomedata/awesome-public-datasetsawesomedata avatar

    awesomedata/awesome-public-datasets

    75,979View on GitHub↗

    This project is a community-maintained, open-access directory of high-quality public datasets. It serves as a centralized reference point for researchers, developers, and data scientists to locate reliable information sources across a wide spectrum of industries and scientific fields. By providing a structured index, the repository facilitates the discovery of data necessary for exploratory analysis, machine learning model training, and the development of data-intensive applications. The directory distinguishes itself through a lightweight, platform-agnostic approach to resource indexing that

    aaron-swartzawesome-public-datasetsdatasets
    View on GitHub↗75,979
  • awslabs/lambda-refarch-iotbackendawslabs avatar

    awslabs/lambda-refarch-iotbackend

    251View on GitHub↗

    Serverless Reference Architecture for creating an IoT Backend

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  • azharimm/kbbi-apiazharimm avatar

    azharimm/kbbi-api

    42View on GitHub↗

    ` ENDPOINT /search?q={query} GET https://kbbi-api-amm.herokuapp.com/search?q=apel `

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  • bbycroft/llm-vizbbycroft avatar

    bbycroft/llm-viz

    5,260View on GitHub↗

    llm-viz is a 3D architecture visualizer and inference simulator for large language models. It provides a visual representation of network topology and the mathematical operations used during the process of generating a response. The tool enables the exploration of internal weight distributions and the layout of layers within a neural network. It facilitates model interpretability and inference debugging by tracking the step-by-step movement of data through the architecture. The system utilizes GPU-accelerated 3D rendering to visualize tensor flow and spatial mappings of weights. It includes

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    View on GitHub↗5,260
  • benedekrozemberczki/awesome-graph-classificationbenedekrozemberczki avatar

    benedekrozemberczki/awesome-graph-classification

    4,805View on GitHub↗

    A collection of important graph embedding, classification and representation learning papers with implementations.

    Pythonattention-mechanismclassification-algorithmdeep-graph-kernels
    View on GitHub↗4,805
  • bitcoin-studio/bitcoin-programming-with-bitcoinjsbitcoin-studio avatar

    bitcoin-studio/Bitcoin-Programming-with-BitcoinJS

    164View on GitHub↗

    Bitcoin Programming with BitcoinJS, Bitcoin Core and LND

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  • bitcoinbook/bitcoinbookB

    bitcoinbook/bitcoinbook

    0View on GitHub↗

    Mastering Bitcoin is a technical book that explains what Bitcoin is and how it works.

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  • bitemyapp/learnhaskellbitemyapp avatar

    bitemyapp/learnhaskell

    8,026View on GitHub↗

    This project is a comprehensive functional programming curriculum and learning resource for Haskell. It provides sequenced educational paths and technical reference guides designed to take developers from beginner to advanced levels of proficiency. The project distinguishes itself through a deep focus on theoretical and technical foundations, offering detailed studies on type theory, category theory, and runtime internals. It includes a dedicated performance handbook for optimizing execution speed and memory management, as well as an ecosystem guide for managing development tools and editor c

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    View on GitHub↗8,026
  • bitsblocks/bitcoin-maximalistB

    bitsblocks/bitcoin-maximalist

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  • bitsblocks/crypto-factsB

    bitsblocks/crypto-facts

    0View on GitHub↗
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  • bitsblocks/get-rich-quick-bibleB

    bitsblocks/get-rich-quick-bible

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  • bitsblocks/islandcoin-whitepaperB

    bitsblocks/islandcoin-whitepaper

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  • bolshchikov/js-must-watchbolshchikov avatar

    bolshchikov/js-must-watch

    13,586View on GitHub↗

    Must-watch videos about javascript

    awesomeawesome-listeducation
    View on GitHub↗13,586
  • 521xueweihan/hellogithub521xueweihan avatar

    521xueweihan/HelloGitHub

    161,590View on GitHub↗

    HelloGitHub is a centralized discovery platform and technical knowledge repository designed to help developers identify high-quality open-source projects, libraries, and infrastructure. It functions as a structured directory that aggregates specialized development tools and educational materials, organizing them by technical domain to facilitate efficient resource discovery and professional development. The platform distinguishes itself through a community-driven curation workflow, where manual editorial oversight filters the broader software ecosystem into thematic collections. This content

    Pythonawesomegithubhellogithub
    View on GitHub↗161,590