Search Results for "google-visualization-python" - Page 4
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Showing 593 open source projects for "google-visualization-python"
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Metaflow
A framework for real-life data science
Metaflow is a human-friendly Python library that helps scientists and engineers build and manage real-life data science projects. Metaflow was originally developed at Netflix to boost productivity of data scientists who work on a wide variety of projects from classical statistics to state-of-the-art deep learning. -
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segment-geospatial
A Python package for segmenting geospatial data with the SAM
The segment-geospatial package draws its inspiration from segment-anything-eo repository authored by Aliaksandr Hancharenka. To facilitate the use of the Segment Anything Model (SAM) for geospatial data, I have developed the segment-anything-py and segment-geospatial Python packages, which are now available on PyPI and conda-forge. My primary objective is to simplify the process of leveraging SAM for geospatial data analysis by enabling users to achieve this with minimal coding effort. I have adapted the source code of segment-geospatial from the segment-anything-eo repository, and credit for its original version goes to Aliaksandr Hancharenka. -
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DoWhy
DoWhy is a Python library for causal inference
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks. Much like machine learning libraries have done for prediction, DoWhy is a Python library that aims to spark causal thinking and analysis. -
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AutoKeras
AutoML library for deep learning
AutoKeras: An AutoML system based on Keras. It is developed by DATA Lab at Texas A&M University. The goal of AutoKeras is to make machine learning accessible to everyone. AutoKeras only support Python 3. If you followed previous steps to use virtualenv to install tensorflow, you can just activate the virtualenv. Currently, AutoKeras is only compatible with Python >= 3.7 and TensorFlow >= 2.8.0. AutoKeras supports several tasks with extremely simple interface. AutoKeras would search for the best detailed configuration for you. ... -
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pmdarima
Statistical library designed to fill the void in Python's time series
A statistical library designed to fill the void in Python's time series analysis capabilities, including the equivalent of R's auto.arima function. -
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Mlxtend
A library of extension and helper modules for Python's data analysis
Mlxtend (machine learning extensions) is a Python library of useful tools for day-to-day data science tasks. -
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imbalanced-learn
A Python Package to Tackle the Curse of Imbalanced Datasets in ML
Imbalanced-learn (imported as imblearn) is an open source, MIT-licensed library relying on scikit-learn (imported as sklearn) and provides tools when dealing with classification with imbalanced classes. -
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GluonTS
Probabilistic time series modeling in Python
GluonTS is a Python package for probabilistic time series modeling, focusing on deep learning based models. GluonTS requires Python 3.6 or newer, and the easiest way to install it is via pip. We train a DeepAR-model and make predictions using the simple "airpassengers" dataset. The dataset consists of a single time-series, containing monthly international passengers between the years 1949 and 1960, a total of 144 values (12 years * 12 months). -
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APKiD
Android Application Identifier for Packers, Protectors and Obfuscators
APKiD gives you information about how an APK was made. It identifies many compilers, packers, obfuscators, and other weird stuff. It's PEiD for Android. -
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omegaml
MLOps simplified. From ML Pipeline ⇨ Data Product without the hassle
omega|ml is the innovative Python-native MLOps platform that provides a scalable development and runtime environment for your Data Products. Works from laptop to cloud. -
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EconML
Python Package for ML-Based Heterogeneous Treatment Effects Estimation
EconML is a Python package for estimating heterogeneous treatment effects from observational data via machine learning. This package was designed and built as part of the ALICE project at Microsoft Research with the goal of combining state-of-the-art machine learning techniques with econometrics to bring automation to complex causal inference problems. -
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Featuretools
An open source python library for automated feature engineering
An open source Python framework for automated feature engineering. Featuretools automatically creates features from temporal and relational datasets. Featuretools uses DFS for automated feature engineering. You can combine your raw data with what you know about your data to build meaningful features for machine learning and predictive modeling. Featuretools provides APIs to ensure only valid data is used for calculations, keeping your feature vectors safe from common label leakage problems. ... -
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PyCaret
An open-source, low-code machine learning library in Python
PyCaret is an open-source, low-code machine learning library in Python that automates machine learning workflows. It is an end-to-end machine learning and model management tool that speeds up the experiment cycle exponentially and makes you more productive. In comparison with the other open-source machine learning libraries, PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few lines only. -
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TorchIO
Medical imaging toolkit for deep learning
...TorchIO is a Python package containing a set of tools to efficiently read, preprocess, sample, augment, and write 3D medical images in deep learning applications written in PyTorch, including intensity and spatial transforms for data augmentation and preprocessing. Transforms include typical computer vision operations such as random affine transformations and also domain-specific ones such as simulation of intensity artifacts due to MRI magnetic field inhomogeneity. -
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tf2onnx
Convert TensorFlow, Keras, Tensorflow.js and Tflite models to ONNX
tf2onnx converts TensorFlow (tf-1.x or tf-2.x), keras, tensorflow.js and tflite models to ONNX via command line or python API. Note: tensorflow.js support was just added. While we tested it with many tfjs models from tfhub, it should be considered experimental. TensorFlow has many more ops than ONNX and occasionally mapping a model to ONNX creates issues. tf2onnx will use the ONNX version installed on your system and installs the latest ONNX version if none is found. -
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RAGFlow
RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine
RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding. It offers a streamlined RAG workflow for businesses of any scale, combining LLM (Large Language Models) to provide truthful question-answering capabilities, backed by well-founded citations from various complex formatted data. -
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FEniCS.jl
A scientific machine learning (SciML) wrapper for the FEniCS
FEniCS.jl is a wrapper for the FEniCS library for finite element discretizations of PDEs. This wrapper includes three parts. Installation and direct access to FEniCS via a Conda installation. Alternatively one may use their current FEniCS installation. A low-level development API and provides some functionality to make directly dealing with the library a little bit easier, but still requires knowledge of FEniCS itself. Interfaces have been provided for the main functions and their... -
18
sktime
A unified framework for machine learning with time series
sktime is a library for time series analysis in Python. It provides a unified interface for multiple time series learning tasks. Currently, this includes time series classification, regression, clustering, annotation, and forecasting. It comes with time series algorithms and scikit-learn compatible tools to build, tune and validate time series models. Our objective is to enhance the interoperability and usability of the time series analysis ecosystem in its entirety. sktime provides a unified interface for distinct but related time series learning tasks. ... -
19
BindsNET
Simulation of spiking neural networks (SNNs) using PyTorch
A Python package used for simulating spiking neural networks (SNNs) on CPUs or GPUs using PyTorch Tensor functionality. BindsNET is a spiking neural network simulation library geared towards the development of biologically inspired algorithms for machine learning. This package is used as part of ongoing research on applying SNNs to machine learning (ML) and reinforcement learning (RL) problems in the Biologically Inspired Neural & Dynamical Systems (BINDS) lab. -
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Adversarial Robustness Toolbox
Adversarial Robustness Toolbox (ART) - Python Library for ML security
Adversarial Robustness Toolbox (ART) is a Python library for Machine Learning Security. ART provides tools that enable developers and researchers to evaluate, defend, certify and verify Machine Learning models and applications against the adversarial threats of Evasion, Poisoning, Extraction, and Inference. ART supports all popular machine learning frameworks (TensorFlow, Keras, PyTorch, MXNet, sci-kit-learn, XGBoost, LightGBM, CatBoost, GPy, etc.), all data types (images, tables, audio, video, etc.) and machine learning tasks (classification, object detection, generation, certification, etc.). -
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Label Studio
Label Studio is a multi-type data labeling and annotation tool
The most flexible data annotation tool. Quickly installable. Build custom UIs or use pre-built labeling templates. Detect objects on image, bboxes, polygons, circular, and keypoints supported. Partition image into multiple segments. Use ML models to pre-label and optimize the process. Label Studio is an open-source data labeling tool. It lets you label data types like audio, text, images, videos, and time series with a simple and straightforward UI and export to various model formats. It can... -
22
AmpliGraph
Python library for Representation Learning on Knowledge Graphs
Open source library based on TensorFlow that predicts links between concepts in a knowledge graph. AmpliGraph is a suite of neural machine learning models for relational Learning, a branch of machine learning that deals with supervised learning on knowledge graphs. -
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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. -
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Daft
Distributed DataFrame for Python designed for the cloud
Daft is a framework for ETL, analytics and ML/AI at scale. Its familiar Python Dataframe API is built to outperform Spark in performance and ease of use. Daft plugs directly into your ML/AI stack through efficient zero-copy integrations with essential Python libraries such as Pytorch and Ray. It also allows requesting GPUs as a resource for running models. Daft runs locally with a lightweight multithreaded backend. -
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BackgroundMattingV2
Real-Time High-Resolution Background Matting
Official repository for the paper Real-Time High-Resolution Background Matting. Our model requires capturing an additional background image and produces state-of-the-art matting results at 4K 30fps and HD 60fps on an Nvidia RTX 2080 TI GPU.
