Label Studio is a multi-type data labeling and annotation tool with standardized output format

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 be used to prepare raw data or improve existing training data to get more accurate ML models. Have a custom dataset? You can customize Label Studio to fit your needs. Read an introductory blog post to learn more. Try out Label Studio What you get from Label Studio Included templates for labeling data in Label Studio Set up machine learning models with Label Studio Integrate Label Studio with your existing tools 

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

pandas is a Python package that provides fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way towards this goal. Easy handling of missing data (represented as NaN, NA, or NaT) in floating point as well as non-floating point data Size mutability: columns can be inserted and deleted from DataFrame and higher dimensional objects Automatic and explicit data alignment: objects can be explicitly aligned to a set of labels, or the user can simply ignore the labels and let Series, DataFrame, etc. automatically align the data for you in computations Powerful, flexible group by functionality to perform split-apply-combine operations on data sets, for both aggregating and transforming data Make it easy to convert ragged, differently-indexed data in other Python and NumPy data structures into DataFrame objects Intelligent label-based slicing, fancy indexing, and subsetting of large data sets Intuitive merging and joining data sets Flexible reshaping and pivoting of data sets Hierarchical labeling of axes (possible to have multiple labels per tick) Robust IO tools for loading data from flat files (CSV and delimited), Excel files, databases, and saving/loading data from the ultrafast HDF5 format Time series-specific functionality: date range generation and frequency conversion, moving window statistics, date shifting and lagging 

Seaborn is a Python visualization library based on matplotlib. It provides a high-level interface for drawing attractive statistical graphics. 

The fundamental package for scientific computing with Python.

GloVe model for distributed word representation

We provide an implementation of the GloVe model for learning word representations, and describe how to download web-dataset vectors or train your own. See the project page or the paper for more information on glove vectors. 

Tensors and Dynamic neural networks in Python with strong GPU acceleration

At a granular level, PyTorch is a library that consists of the following components:. A replacement for NumPy to use the power of GPUs. A deep learning research platform that provides maximum flexibility and speed. 

Keras is a deep learning API written in Python, running on top of the machine learning platform TensorFlow. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result as fast as possible is key to doing good research. Simple -- but not simplistic. Keras reduces developer cognitive load to free you to focus on the parts of the problem that really matter. Flexible -- Keras adopts the principle of progressive disclosure of complexity: simple workflows should be quick and easy, while arbitrarily advanced workflows should be possible via a clear path that builds upon what you've already learned. Powerful -- Keras provides industry-strength performance and scalability: it is used by organizations and companies including NASA, YouTube, and Waymo. 

Course Reco

Data Analysis

label-studioby heartexlabs

label-studioby heartexlabs

pandasby pandas-dev

pandasby pandas-dev

matplotlibby matplotlib

matplotlibby matplotlib

seabornby mwaskom

seabornby mwaskom

Machine Learning

numpyby numpy

numpyby numpy

scipyby scipy

scipyby scipy

GloVeby stanfordnlp

GloVeby stanfordnlp

pytorchby pytorch

pytorchby pytorch

kerasby keras-team

kerasby keras-team