Search Results for "python data analysis" - Page 9
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Showing 272 open source projects for "python data analysis"
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RecNN
Reinforced Recommendation toolkit built around pytorch 1.7
This is my school project. It focuses on Reinforcement Learning for personalized news recommendation. The main distinction is that it tries to solve online off-policy learning with dynamically generated item embeddings. I want to create a library with SOTA algorithms for reinforcement learning recommendation, providing the level of abstraction you like. -
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Optimized Storage for temporal Data
open Optimized Storage of time series data
Beta version. Base class for optimized storage of time series data. Uses any kind of relational database. Cross plateform with multiple languages (C++, C#, Java). Conditional storage based on value variation : DeltaValue and DeltaTime params. Get back data without losts. -
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I3D models trained on Kinetics
Convolutional neural network model for video classification
Kinetics-I3D, developed by Google DeepMind, provides trained models and implementation code for the Inflated 3D ConvNet (I3D) architecture introduced in the paper “Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset” (CVPR 2017). The I3D model extends the 2D convolutional structure of Inception-v1 into 3D, allowing it to capture spatial and temporal information from videos for action recognition. This repository includes pretrained I3D models on the Kinetics dataset, with... -
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AES Everywhere
Cross Language AES 256 Encryption Library
AES Everywhere is Cross Language Encryption Library that provides the ability to encrypt and decrypt data using a single algorithm in different programming languages and on different platforms. This is an implementation of the AES algorithm, specifically CBC mode, with 256-bit key length and PKCS7 padding. It implements OpenSSL-compatible cryptography with randomly generated salt. -
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MLBox
MLBox is a powerful Automated Machine Learning python library
MLBox is a powerful Automated Machine Learning python library. Fast reading and distributed data preprocessing/cleaning/formatting. Highly robust feature selection and leak detection. Accurate hyper-parameter optimization in high-dimensional space. State-of-the-art predictive models for classification and regression (Deep Learning, Stacking, LightGBM,...) Prediction with model interpretation. -
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RobotsDisallowed
A curated list of the most common and most interesting robots.txt
RobotsDisallowed is a public catalog that tracks websites and organizations explicitly blocking AI and web-scraping crawlers in their robots.txt or related mechanisms. It focuses on documenting the growing trend of content owners asserting control over how their data is used for model training and automated harvesting. The project aggregates domains, notes the targeted bots or user agents, and surfaces patterns for researchers, policymakers, and tool builders. It serves both as a... -
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Personal Blog
One article per week, the content is concise, neither salty nor light
Personal Blog holds the source structure and article index for the author’s personal technical blog, which is closely tied to the “芋道源码” WeChat public account. It uses Markdown files and a static-site setup (with configuration like _config.yml) to organize posts about Java back-end engineering, distributed systems, and source-code deep dives. The README and index emphasize that the blog (in this repo) is paused and that new content is primarily delivered via the WeChat channel, but the... -
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data-science-ipython-notebooks
Data science Python notebooks: Deep learning
Data Science IPython Notebooks is a broad, curated set of Jupyter notebooks covering Python, data wrangling, visualization, machine learning, deep learning, and big data tools. It aims to be a practical map of the ecosystem, showing hands-on examples with libraries such as NumPy, pandas, matplotlib, scikit-learn, and others. Many notebooks introduce concepts step by step, then apply them to real datasets so readers can see techniques in action. -
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Django REST Pandas
Serves up Pandas dataframes via the Django REST Framework
Django REST Pandas (DRP) provides a simple way to generate and serve pandas DataFrames via the Django REST Framework. The resulting API can serve up CSV (and a number of other formats for consumption by a client-side visualization tool like d3.js. The design philosophy of DRP enforces a strict separation between data and presentation. This keeps the implementation simple, but also has the nice side effect of making it trivial to provide the source data for your visualizations. This... -
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Functional, Data Science Intro To Python
[tutorial]A functional, Data Science focused introduction to Python
The first section is an intentionally brief, functional, data science-centric introduction to Python. The assumption is a someone with zero experience in programming can follow this tutorial and learn Python with the smallest amount of information possible. The sections after that, involve varying levels of difficulty and cover topics as diverse as Machine Learning, Linear Optimization, build systems, command line tools, recommendation engines, Sentiment Analysis and Cloud Computing. -
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Autologging
Easier logging and tracing of Python functions and class methods.
Autologging eliminates boilerplate logging setup code and tracing code, and provides a means to separate application logging from program flow and data tracing. Autologging provides two decorators and a custom log level: "autologging.logged" decorates a class to create a __log member. By default, the logger is named for the class's containing module and name (e.g. "my.module.ClassName"). "autologging.traced" decorates a class to provide automatic CALL/RETURN tracing for all class, static, and instance methods, as well as the special __init__ method (by default) "autologging.TRACE" is a custom log level (lower than logging.DEBUG) that is registered with the Python logging module when autologging is imported -
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Skater
Python library for model interpretation/explanations
Skater is a unified framework to enable Model Interpretation for all forms of the model to help one build an Interpretable machine learning system often needed for real-world use-cases(** we are actively working towards to enabling faithful interpretability for all forms models). It is an open-source python library designed to demystify the learned structures of a black box model both globally(inference on the basis of a complete data set) and locally(inference about an individual prediction). The concept of model interpretability in the field of machine learning is still new, largely subjective, and, at times, controversial. Model interpretation is the ability to explain and validate the decisions of a predictive model to enable fairness, accountability, and transparency in algorithmic decision-making. ... -
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Ponder
C++ reflection library with Lua binding, and JSON and XML
...It provides an abstraction for most of the high-level concepts of C++ like classes, enumerations, properties, functions, and objects. By wrapping all these concepts into abstract structures, Ponder provides an extra layer of flexibility to programs and allows them to expose and manipulate their data structures at runtime. Many applications can take advantage of Ponder, in order to automate tasks that would otherwise require a huge amount of work. For example, Ponder can be used to expose and edit objects' attributes in a graphical user interface. It can also be used to do automatic binding of C++ classes to script languages such as Python or Lua. ... -
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Mixup-CIFAR10
mixup: Beyond Empirical Risk Minimization
mixup-cifar10 is the official PyTorch implementation of “mixup: Beyond Empirical Risk Minimization” (Zhang et al., ICLR 2018), a foundational paper introducing mixup, a simple yet powerful data augmentation technique for training deep neural networks. The core idea of mixup is to generate synthetic training examples by taking convex combinations of pairs of input samples and their labels. By interpolating both data and labels, the model learns smoother decision boundaries and becomes more... -
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cnn-text-classification-tf
Convolutional Neural Network for Text Classification in Tensorflow
The cnn-text-classification-tf repository by Denny Britz is a well-known educational implementation of convolutional neural networks for text classification using TensorFlow, aimed at helping developers and researchers understand how CNNs can be applied to natural language processing tasks. Based loosely on Kim’s influential paper on CNNs for sentence classification, this codebase demonstrates how to preprocess text data, convert words into learned embeddings, and apply multiple convolution... -
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Zhao
A compilation of "The Princely Party Relationship Network"
zhao is a repository that consolidates research, data, and insights related to Zhao, which is likely an individual’s research collection, notes, or curated resources on deep learning, AI, or computational topics (name and content context suggest specialized study). The project may include code examples, experiment results, references to academic papers, mathematical notes, and supporting scripts to explore specific ML methods, benchmarks, or theoretical findings. Because it aggregates... -
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FlexibleParser_Java
Group of multi-purpose Java-converted-from-C# parsing libraries.
FlexibleParser (Java) is currently formed by the following independent libraries/parts: - UnitParser. It allows to easily deal with a wide variety of situations involving units of measurement. - NumberParser. It provides a common framework for all the .NET numeric types. AUTHORSHIP & COPYRIGHT I, Alvaro Carballo Garcia (varocarbas), am the sole author of each single bit of this code. All the contents of this repository can be considered public domain. For more information about my... -
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FlexibleParser
FlexibleParser is a group of multi-purpose .NET parsing libraries.
FlexibleParser (C#/.NET) is currently formed by the following independent libraries/parts: - UnitParser. It allows to easily deal with a wide variety of situations involving units of measurement. - NumberParser. It provides a common framework for all the .NET numeric types. - DateParser. It enhances the default .NET date/time support. AUTHORSHIP & COPYRIGHT I, Alvaro Carballo Garcia (varocarbas), am the sole author of each single bit of this code. All the contents of this... -
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pivottablejs
Drag’n’drop Pivot Tables and Charts for Jupyter/IPython Notebook
PivotTable.js is a Javascript Pivot Table and Pivot Chart library with drag-drop interactivity, and it can now be used with Jupyter/IPython Notebook via the pivottablejs module. I first built PivotTable.js with a plan to build an in-browser data analysis tool, and got as far as one where you could load up a CSV file in the browser for display. Since then, however, the Jupyter project has gathered steam and now provides a browser-based interface to some of the most powerful data processing libraries in the world, so it makes sense to interface with it. -
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Deeplearning-papernotes
Summaries and notes on Deep Learning research papers
Deeplearning-papernotes is an implementation of Convolutional Neural Networks for sentence and text classification in TensorFlow, based on a well-known research paper that applies CNN architectures to natural language processing tasks with strong performance in sentiment analysis and similar classification problems. The repository provides the complete network definition, including an embedding layer to convert words into dense representations, convolution and max-pooling layers to extract informative features, and a final softmax classifier to distinguish between target classes. It includes data preprocessing helpers, training scripts, and configuration options so developers can experiment with different filter sizes, dropout rates, and hyperparameters to optimize performance for their dataset. -
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LBSP
Real-Time Processing Library for OSHW Biomedical Sensors
Applications involving data acquisition from sensors need samples at a preset frequency rate, the filtering out of noise and/or analysis of certain frequency components. We propose a novel software architecture based on open-software hardware platforms which allows programmers to create data streams from input channels and easily implement filters and frequency analysis objects. -
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chemfiles
Modern library for chemistry file reading and writing
Chemfiles is a modern and high-quality library for reading and writing trajectory files created by computational chemistry simulations program. These trajectories contains atomic positions, velocities, names, topology and sometimes more. Running simulations produce enormous amounts of data, which has to be post-processed to extract physical information about the simulated system. Chemfiles provides an interface to access this information which is - unified: the same code will work with... -
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DataScienceR
a curated list of R tutorials for Data Science, NLP
The DataScienceR repository is a curated collection of tutorials, sample code, and project templates for learning data science using the R programming language. It includes an assortment of exercises, sample datasets, and instructional code that cover the core steps of a data science project: data ingestion, cleaning, exploratory analysis, modeling, evaluation, and visualization. Many of the modules demonstrate best practices in R, such as using the tidyverse, R Markdown, modular scripting, and reproducible workflows. ... -
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pyCGNS
A Python package for CGNS
pyCGNS is now on github: https://pycgns.github.io/index.html https://github.com/pyCGNS/pyCGNS -
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BWEM
Fast and robust map analyser for Brood War.
Brood War Easy Map is a C++ library that analyses Brood War's maps and provides relevant information such as areas, choke points and base locations. It is built on top of the BWAPI library. It first aims at simplifying the development of bots for Brood War, but can be used for any task requiring high level map information. It can be used as a replacement for the BWTA2 add-on, as it performs faster and shows better robustness while providing similar information.
