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Python Handout
Turn Python scripts into handouts with Markdown and figures
Handout is a lightweight library for embedding rich, interactive components such as exercises, charts, and interactive diagrams directly into static documents like Markdown, Jupyter notebooks, or static HTML pages, enabling authors to create more engaging technical handouts, tutorials, and interactive essays. It’s particularly aimed at educators, presenters, and researchers who want to make their written material come alive with runnable demonstrations and interactive problem sets without... -
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lzhw
LZHW Windows command line lossless compression tool for tabular files
LZHW Command Line Lossless Compression Tool is a Windows command line tool used to compress and decompress files from and to any form, csv, excel etc without any dependencies or installations. Using an optimized algorithm (LZHW) developed from Lempel-Ziv, Huffman and LZ-Welch algorithms. The tool can work in parallel and most of its code is written in Cython, so it is pretty fast. It is based on python lzhw library. Full tool documentation can be found at:... -
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Stable Baselines
A fork of OpenAI Baselines, implementations of reinforcement learning
Stable Baselines is a set of improved implementations of reinforcement learning algorithms based on OpenAI Baselines. You can read a detailed presentation of Stable Baselines in the Medium article. These algorithms will make it easier for the research community and industry to replicate, refine, and identify new ideas, and will create good baselines to build projects on top of. We expect these tools will be used as a base around which new ideas can be added, and as a tool for comparing a new... -
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Optimus
Agile Data Preparation Workflows made easy with Pandas
Easily write code to clean, transform, explore and visualize data using Python. Process using a simple API, making it easy to use for newcomers. More than 100 functions to handle strings, process dates, urls and emails. Easily plot data from any size. Out-of-box functions to explore and fix data quality. Use the same code to process your data in your laptop or in a remote cluster of GPUs. -
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EasyArch
Arch Linux Installer ISO
A simple yet full featured Archlinux installer ISO. Minimum system requirements- Processor - 2 core 64 bit Ram - 1 GB HDD space - 10 GB If you are looking for another linux distro, then you are at wrong place, this is not a new or separate distribution. It is just a live ISO to provide simple and easy way to get Archlinux up and running in very little time and with or without internet connection. Yes, you read it right, you can install Archlinux without internet with this ISO.... -
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Linux-Intelligent-Ocr-Solution
Easy-OCR solution and Tesseract trainer for GNU/Linux
Linux-intelligent-ocr-solution Lios is a free and open source software for converting print in to text using either scanner or a camera, It can also produce text out of scanned images from other sources such as Pdf, Image, Folder containing Images or screenshot. Program is given total accessibility for visually impaired. A Tesseract Trainer GUI is also shipped with this package. Forum : https://groups.google.com/forum/#!forum/lios Video Tutorial :... -
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ebfformat
An Efficient Binary data Format
EBF, which stands for Efficient Binary Format, is a binary file format for reading and writing binary data easily. Reading writing routines are currently available in C,C++,Fortran,Java, Python, IDL, MATLAB. A program called ebftkpy which has a set of utility functions to work with the .ebf files , e.g., viewing the contents and getting a summary, is also provided. The EBF specification is designed to be concise and easy to understand to make it easier for others to write their own code... -
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repo2docker GitHub Action
A GitHub action to build data science environment images
Trigger repo2docker to build a Jupyter enabled Docker image from your GitHub repository and push this image to a Docker registry of your choice. This will automatically attempt to build an environment from configuration files found in your repository. Images generated by this action are automatically tagged with both latest and <SHA> corresponding to the relevant commit SHA on GitHub. Both tags are pushed to the Docker registry specified by the user. If an existing image with the latest tag... -
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MMdnn
Tools to help users inter-operate among deep learning frameworks
MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML. MMdnn is a comprehensive and cross-framework tool to convert, visualize and diagnose deep learning (DL) models. The "MM" stands for model management, and "dnn" is the acronym of deep neural network. We implement a universal converter to convert DL models between frameworks,... -
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Free Queue Manager
Web based python-flask Queue management system
A web based management system developed for the purpose of easing the process of orgnizing queues and lines. Like many other (QMS)s Queue Management Systems, FQM does provide a basic dashboard to allow the users of the system and customers alike to interact with the system via a basic yet simple user interface . Brief user guide can be found on https://fqms.github.io/images/user_guide.pdf -
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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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KlayGE
KlayGE is a cross-platform open source game engine
KlayGE is a cross-platform open source game engine with plugin-based architecture. It's started since 2003. The explicit goal of KlayGE is: to arm your engine with cutting-edge technology. It provides a framework to make game development, testing, porting as simple as possible. Before compiling KlayGE, Python 3.x+ and CMake 3.13+ must be installed first. Then you can run Build.py to build the whole code base. -
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StellarGraph
Machine Learning on Graphs
StellarGraph is a Python library for machine learning on graphs and networks. The StellarGraph library offers state-of-the-art algorithms for graph machine learning, making it easy to discover patterns and answer questions about graph-structured data. It can solve many machine learning tasks. Graph-structured data represent entities as nodes (or vertices) and relationships between them as edges (or links), and can include data associated with either as attributes. For example, a graph can... -
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Brand new cheatsheets and handouts
Matplotlib 3.1 cheat sheet
The Brand new cheatsheets and handouts repo is a compact, quick-reference summary of the most commonly used plotting commands and configurations in Matplotlib, intended to serve as a handy reference for experienced users who want to recall syntax or find the right function without digging into full documentation. It lays out common use cases (plot types, styling, figure configuration, saving/exporting, subplot layout, etc.) in a concise and organized format — often serving as a “cheat sheet”... -
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DETR
End-to-end object detection with transformers
PyTorch training code and pretrained models for DETR (DEtection TRansformer). We replace the full complex hand-crafted object detection pipeline with a Transformer, and match Faster R-CNN with a ResNet-50, obtaining 42 AP on COCO using half the computation power (FLOPs) and the same number of parameters. Inference in 50 lines of PyTorch. What it is. Unlike traditional computer vision techniques, DETR approaches object detection as a direct set prediction problem. It consists of a set-based... -
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SageMaker Containers
Create SageMaker-compatible Docker containers
Amazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models. To train a model, you can include your training script and dependencies in a Docker container that runs your training code. A container provides an effectively isolated environment, ensuring a consistent runtime and reliable training process. The SageMaker Training Toolkit can be easily added to... -
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ENAS in PyTorch
PyTorch implementation of "Efficient Neural Architecture Search
ENAS in PyTorch is a PyTorch implementation of Efficient Neural Architecture Search (ENAS), a method that automates the design of neural network architectures through reinforcement learning and parameter sharing. The repository demonstrates how a controller network can explore a large search space and discover high-performing architectures while dramatically reducing the computational cost traditionally associated with neural architecture search. It is primarily intended as a research and... -
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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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Dive into DL PyTorch
This project changes the MXNet implementation
Dive into DL PyTorch is a PyTorch adaptation of the Chinese edition of the educational book Dive into Deep Learning. It replaces the book's original MXNet implementations with executable PyTorch examples while preserving the broader instructional material. Chapter code is organized as Jupyter notebooks so learners can run and modify examples interactively. The accompanying documentation is stored in Markdown and published as a browsable Docsify website. The material is intended for beginners... -
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...Installation Videos! Part 1: http://youtu.be/rnv2VLcG-eI Part 2: http://youtu.be/eFudbMWHNlQ Special thanks to Wells Oliver for the code for downloading Retrosheet files. And the Chadwick project for its Retrosheet tools. https://sourceforge.net/projects/chadwick/?source=recommended
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KwaMoja
Out of Africa comes KwaMoja. A fresh start for webERP!
KwaMoja is designed to help you manage your business in a more efficient and profitable manner. It combines your book keeping needs with all the tools to plan your inventory purchases. It will let you know when your factory needs to start manufacturing parts in order to keep up with orders. It will help you better manage your cash flow. All these and more will help your business become more efficient, and greater efficiency will equal greater profits. KwaMoja is a fork of the once... -
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Tensor2Tensor
Library of deep learning models and datasets
Deep Learning (DL) has enabled the rapid advancement of many useful technologies, such as machine translation, speech recognition and object detection. In the research community, one can find code open-sourced by the authors to help in replicating their results and further advancing deep learning. However, most of these DL systems use unique setups that require significant engineering effort and may only work for a specific problem or architecture, making it hard to run new experiments and... -
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TxPB
TemporalX gRPC and Documentation Resources
TxPB is the main source of information for using TemporalX, and includes protocol buffer definitions, generated code, and documentation. If you are looking for the protocol buffer definitions, check the pb folder. If you want bindings in other languages, feel free to open a github issue. Alternatively you can generate language bindings independently as long as the languages you are using support the appropriate protocol buffer and gRPC versions. -
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Reliable Metrics for Generative Models
Code base for the precision, recall, density, and coverage metrics
Reliable Fidelity and Diversity Metrics for Generative Models (ICML 2020). Devising indicative evaluation metrics for the image generation task remains an open problem. The most widely used metric for measuring the similarity between real and generated images has been the Fréchet Inception Distance (FID) score. Because it does not differentiate the fidelity and diversity aspects of the generated images, recent papers have introduced variants of precision and recall metrics to diagnose those... -
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Kopf
A Python framework to write Kubernetes operators
Kopf —Kubernetes Operator Pythonic Framework, is a framework and a library to make Kubernetes operator's development easier, just in a few lines of Python code. The main goal is to bring the Domain-Driven Design to the infrastructure level, with Kubernetes being an orchestrator/database of the domain objects (custom resources), and the operators containing the domain logic (with no or minimal infrastructure logic).
