Search Results for "notebook" - Page 8
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Showing 386 open source projects for "notebook"
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LLM Training Puzzles
What would you do with 1000 H100s
...The hands-on format emphasizes deriving behavior rather than memorizing framework commands. Google Colab is recommended so readers can copy the notebook and begin experimenting quickly. The project belongs to a broader puzzle series covering tensors, automatic differentiation, Transformers, GPUs, and related topics. -
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Cr OS Linux Flex Beta (Discontinued)
The Discontinuation of Cr OS Linux was an Updated DIY Distro Fan Page
...We call it the Chrome Beta plated OS, because it is stable and shiny. This open source project provides a lightweight Linux distribution similar to Google Chrome OS for the best web browsing experience on any Desktop PC, Netbook, Notebook, or Laptop. With Cr OS Linux KDE, you'll have cutting edge technology being installed on your system. You'll also have Security, Privacy, and Anonymous Protection on your PC. It's Fast, Very Fast, & Extremely Safe to use. Cr OS Linux 12 is Continued from Version 2.4.1290 Cr OS Linux is not related to Google. This service is powered by KDE Neon (Read Here> https://sourceforge.net/p/cr-os-linux-beta-lts/notes/) Updated: 02/02/2024 -
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Complete Machine Learning Package
A comprehensive machine learning repository containing 30+ notebooks
...The project includes more than thirty notebooks that cover a wide range of topics including data analysis, statistical modeling, neural networks, and deep learning. Each notebook introduces theoretical ideas and then demonstrates how to implement them using Python libraries commonly used in data science, such as NumPy, pandas, scikit-learn, and TensorFlow. The repository also includes examples related to natural language processing, computer vision, and data visualization, giving learners exposure to several subfields of machine learning. ... -
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YCBlogs
A comprehensive collection of technical blog notes
...Large collections of technical questions make it useful for review and interview preparation. Most content is stored as Markdown, allowing the collection to function as a searchable long-term engineering notebook. -
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hloc
Visual localization made easy with hloc
...We provide step-by-step guides to localize with Aachen, InLoc, and to generate reference poses for your own data using SfM. Just download the datasets and you're reading to go! The notebook pipeline_InLoc.ipynb shows the steps for localizing with InLoc. It's much simpler since a 3D SfM model is not needed. We show in pipeline_SfM.ipynb how to run 3D reconstruction for an unordered set of images. This generates reference poses, and a nice sparse 3D model suitable for localization with the same pipeline as Aachen. -
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ipyvolume
3d plotting for Python in the Jupyter notebook
3d plotting for Python in the Jupyter notebook based on IPython widgets using WebGL. Create quiver plots (like scatter, but with an arrow pointing in a particular direction). Render in the Jupyter notebook, or create a standalone html page (or snippet to embed in your page). Render in stereo, for virtual reality with Google Cardboard. Animate in d3 style, for instance, if the x coordinates or color of a scatter plots changes. -
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TensorFlow Ranking
Learning to rank in TensorFlow
...We envision that this library will provide a convenient open platform for hosting and advancing state-of-the-art ranking models based on deep learning techniques, and thus facilitate both academic research and industrial applications. We provide a demo, with no installation required, to get started on using TF-Ranking. This demo runs on a colaboratory notebook, an interactive Python environment. Using sparse features and embeddings in TF-Ranking. -
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SageMaker Experiments Python SDK
Experiment tracking and metric logging for Amazon SageMaker notebooks
Experiment tracking in SageMaker Training Jobs, Processing Jobs, and Notebooks. SageMaker Experiments is an AWS service for tracking machine learning Experiments. The SageMaker Experiments Python SDK is a high-level interface to this service that helps you track Experiment information using Python. Experiment tracking powers the machine learning integrated development environment Amazon SageMaker Studio. Experiment: A collection of related Trials. Add Trials to an Experiment that you wish to... -
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Python Data Science Handbook
Python Data Science Handbook: full text in Jupyter Notebooks
...The project is designed for data scientists, researchers, and anyone transitioning into Python-based data work; it assumes you already know basic Python and focuses more on how to use the ecosystem effectively. Each chapter is a standalone Jupyter notebook, with runnable code, explanatory prose, visuals, and examples showing how to handle data-wrangling, exploratory data analysis, machine learning workflows, and visualization. The repository is freely available and the code is released under the MIT license; the textual content is released under a Creative Commons license. Users can also launch the notebooks in Google Colab or Binder directly, making it extremely accessible. -
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KotlinDL
High-level Deep Learning Framework written in Kotlin
KotlinDL is a high-level Deep Learning API written in Kotlin and inspired by Keras. Under the hood, it uses TensorFlow Java API and ONNX Runtime API for Java. KotlinDL offers simple APIs for training deep learning models from scratch, importing existing Keras and ONNX models for inference, and leveraging transfer learning for tailoring existing pre-trained models to your tasks. This project aims to make Deep Learning easier for JVM and Android developers and simplify deploying deep learning... -
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vim-jukit
Jupyter-Notebook inspired Neovim/Vim Plugin
REPL plugin and Jupyter-Notebook alternative for (Neo)Vim. This plugin is aimed at users in search for a REPL plugin with lots of additional features. -
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Alphafold
Open source code for AlphaFold
...Please also refer to the Supplementary Information for a detailed description of the method. You can use a slightly simplified version of AlphaFold with this Colab notebook or community-supported versions. The total download size for the full databases is around 415 GB and the total size when unzipped is 2.2 TB. Please make sure you have a large enough hard drive space, bandwidth and time to download. We recommend using an SSD for better genetic search performance. -
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Weave
Scientific reports/literate programming for Julia
...You can write your documentation and code in an input document using Markdown, Noweb or ordinal Julia script syntax, and then use the weave function to execute code and generate an output document while capturing results and figures. Supports various output document formats: HTML, PDF, GitHub markdown, Jupyter Notebook, MultiMarkdown, Asciidoc and reStructuredText. -
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Bayesian Methods for Hackers
An introduction to Bayesian methods + probabilistic programming
...It is written from a computation-first perspective, prioritizing intuition, examples, and executable notebooks over heavy mathematical formalism. The project introduces readers to uncertainty, Bayesian modeling, MCMC, priors, posteriors, and real-world probabilistic reasoning. It includes notebook-based chapters that let learners run and modify examples directly. The material is especially useful for programmers, data scientists, and technically curious readers who want to learn Bayesian methods through code. It remains a widely referenced entry point for making Bayesian statistics feel practical and approachable. -
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CARTOframes
CARTO Python package for data scientists
A Python package for integrating CARTO maps, analysis, and data services into data science workflows. Python data analysis workflows often rely on the de facto standards pandas and Jupyter notebooks. Integrating CARTO into this workflow saves data scientists time and energy by not having to export datasets as files or retain multiple copies of the data. Instead, CARTOframes give the ability to communicate reproducible analysis while providing the ability to gain from CARTO's services like... -
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System Design Notebook
Learn System Design step by step
System Design Notebook is a structured, personal knowledge base for learning and practicing system design, written in a way that mirrors real interview and on-the-job thinking. Instead of being a single long article, it’s split into topics like scalability, load balancing, data partitioning, caching, availability, consistency, and communication patterns, so you can study them in isolation. -
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codi.vim
The interactive scratchpad for hackers
...It focuses on minimal setup so you can open a buffer, set a filetype, and start experimenting immediately. Because results update incrementally, it is useful for learning, debugging, or documenting small examples. It turns Vim into a lightweight computational notebook while keeping the modal editing experience intact. -
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ArtLine
Deep learning tool that converts portrait photos into line art
...An extended version integrates ControlNet, allowing users to guide the output style through textual instructions alongside the input image. ArtLine is primarily distributed as Jupyter notebooks, making it accessible for experimentation and interactive usage, especially in notebook-based environments. While the system can produce impressive results, it is sensitive to factors like lighting, background complexity, and image quality, and still struggles with elements such as shadows and fine details like hair. -
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Hydrogen
Run code interactively, inspect data, and plot
...By bringing the interactive style of Light Table to the rock-solid usability of Atom, Hydrogen makes it easy to write code the way you want to. You also may be interested in our latest project – interact – a desktop application that wraps up the best of the web-based Jupyter notebook. Watch expressions let you keep track of variables and re-run snippets after every change. Completions from the running kernel, just like autocomplete in the Chrome dev tools. Code can be inspected to show useful information provided by the running kernel. One kernel per language (so you can run snippets from several files, all in the same namespace). -
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abstract2paper
Auto-generate an entire paper from a prompt or abstract using NLP
...In its unmodified state, the demo notebooks use the abstract from the GPT-3 paper as the "seed" for a new paper. Each time you run the notebook you'll get a new result. -
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starboard-notebook
In-browser literate notebooks
starboard-notebook is an in-browser literate notebook runtime that enables users to write, edit, and interact with computational documents directly in the browser without needing a traditional backend notebook server. The project is built around an innovative runtime that interprets code cells and markdown in a way that feels fluid and interactive, often bridging concepts from classical notebook tools with modern web technologies. -
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Bloxs
Build dashboards in Jupyter Notebook with numeric and chart boxes
Bloxs is a simple Python package that helps you display information in an attractive way (formed in blocks). Perfect for building dashboards, reports and apps in the notebook. -
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Amazon SageMaker Operators Kubernetes
Amazon SageMaker operator for Kubernetes
...With SageMaker, data scientists and developers can quickly and easily build and train machine learning models, and then directly deploy them into a production-ready hosted environment. It provides an integrated Jupyter authoring notebook instance for easy access to your data sources for exploration and analysis, so you don't have to manage servers. It also provides common machine learning algorithms that are optimized to run efficiently against extremely large data in a distributed environment. With native support for bring-your-own-algorithms and frameworks, SageMaker offers flexible distributed training options that adjust to your specific workflows. ... -
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lauetoolsnn
LaueNN- neural network routine to index Laue patterns
An autonomous feed-forward neural network (FFNN) model to predict the HKL in single/multi-grain/multi-phase Laue patterns with high efficiency and accuracy is introduced. -
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Pattern Recognition and Machine Learning
Repository of notes, code and notebooks in Python
Pattern Recognition and Machine Learning is an open-source repository that provides Python implementations and interactive notebooks for algorithms presented in the book Pattern Recognition and Machine Learning by Christopher Bishop. The project recreates many of the mathematical concepts and diagrams from the book using executable Jupyter notebooks, allowing readers to experiment directly with the algorithms described in the text. Each section of the repository corresponds to chapters in...
