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NLP-Guide

Natural Language Processing (NLP). Covering topics such as Tokenization, Part Of Speech tagging (POS), Machine translation, Named Entity Recognition (NER), Classification, and Sentiment analysis.
https://github.com/mikeroyal/NLP-Guide

Last synced: 7 days ago
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  • MATLAB Tools, Libraries, Frameworks

    • Robotics Toolbox™ - holonomic vehicle. The Toolbox also including a detailed Simulink model for a quadrotor flying robot.
    • Deep Learning Toolbox™ - term memory (LSTM) networks to perform classification and regression on image, time-series, and text data. You can build network architectures such as generative adversarial networks (GANs) and Siamese networks using automatic differentiation, custom training loops, and shared weights. With the Deep Network Designer app, you can design, analyze, and train networks graphically. It can exchange models with TensorFlow™ and PyTorch through the ONNX format and import models from TensorFlow-Keras and Caffe. The toolbox supports transfer learning with DarkNet-53, ResNet-50, NASNet, SqueezeNet and many other pretrained models.
    • Reinforcement Learning Toolbox™ - making algorithms for complex applications such as resource allocation, robotics, and autonomous systems.
    • Deep Learning HDL Toolbox™ - built bitstreams for running a variety of deep learning networks on supported Xilinx® and Intel® FPGA and SoC devices. Profiling and estimation tools let you customize a deep learning network by exploring design, performance, and resource utilization tradeoffs.
    • Model Predictive Control Toolbox™ - loop simulations, you can evaluate controller performance.
    • Vision HDL Toolbox™ - streaming algorithms for the design and implementation of vision systems on FPGAs and ASICs. It provides a design framework that supports a diverse set of interface types, frame sizes, and frame rates. The image processing, video, and computer vision algorithms in the toolbox use an architecture appropriate for HDL implementations.
    • SoC Blockset™
    • Wireless HDL Toolbox™ - verified, hardware-ready Simulink® blocks and subsystems for developing 5G, LTE, and custom OFDM-based wireless communication applications. It includes reference applications, IP blocks, and gateways between frame and sample-based processing.
    • ThingSpeak™ - of-concept IoT systems that require analytics.
    • hctsa - series analysis using Matlab.
    • YALMIP
    • hctsa - series analysis using Matlab.
    • MATLAB Schemer
    • LRSLibrary - Rank and Sparse Tools for Background Modeling and Subtraction in Videos. The library was designed for moving object detection in videos, but it can be also used for other computer vision and machine learning problems.
    • SEA-MAT
    • Gramm - level interface to produce publication-quality plots of complex data with varied statistical visualizations. Gramm is inspired by R's ggplot2 library.
    • Image Processing Toolbox™ - standard algorithms and workflow apps for image processing, analysis, visualization, and algorithm development. You can perform image segmentation, image enhancement, noise reduction, geometric transformations, image registration, and 3D image processing.
    • GNU Octave - level interpreted language, primarily intended for numerical computations. It provides capabilities for the numerical solution of linear and nonlinear problems, and for performing other numerical experiments. It also provides extensive graphics capabilities for data visualization and manipulation.

  • ML Frameworks, Libraries, and Tools

    • PyTorch
    • Amazon SageMaker
    • Azure Databricks - based big data analytics service designed for data science and data engineering. Azure Databricks, sets up your Apache Spark environment in minutes, autoscale, and collaborate on shared projects in an interactive workspace. Azure Databricks supports Python, Scala, R, Java, and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch, and scikit-learn.
    • Apple CoreML - tune models, all on the user's device. A model is the result of applying a machine learning algorithm to a set of training data. You use a model to make predictions based on new input data.
    • Apache OpenNLP - source library for a machine learning based toolkit used in the processing of natural language text. It features an API for use cases like [Named Entity Recognition](https://en.wikipedia.org/wiki/Named-entity_recognition), [Sentence Detection](), [POS(Part-Of-Speech) tagging](https://en.wikipedia.org/wiki/Part-of-speech_tagging), [Tokenization](https://en.wikipedia.org/wiki/Tokenization_(data_security)) [Feature extraction](https://en.wikipedia.org/wiki/Feature_extraction), [Chunking](https://en.wikipedia.org/wiki/Chunking_(psychology)), [Parsing](https://en.wikipedia.org/wiki/Parsing), and [Coreference resolution](https://en.wikipedia.org/wiki/Coreference).
    • Open Neural Network Exchange(ONNX) - in operators and standard data types.
    • Apache MXNet
    • AutoGluon - accuracy deep learning models on tabular, image, and text data.
    • Anaconda
    • Scikit-Learn
    • Jupyter Notebook - source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Jupyter is used widely in industries that do data cleaning and transformation, numerical simulation, statistical modeling, data visualization, data science, and machine learning.
    • Apache Spark - scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
    • Apache PredictionIO
    • BigDL
    • Eclipse Deeplearning4J (DL4J) - based(Scala, Kotlin, Clojure, and Groovy) deep learning application. This means starting with the raw data, loading and preprocessing it from wherever and whatever format it is in to building and tuning a wide variety of simple and complex deep learning networks.
    • XGBoost
    • Tensorflow_macOS - optimized version of TensorFlow and TensorFlow Addons for macOS 11.0+ accelerated using Apple's ML Compute framework.
    • PlaidML
    • Caffe
    • Theano - dimensional arrays efficiently including tight integration with NumPy.
    • nGraph - of-use to AI developers.
    • Cluster Manager for Apache Kafka(CMAK)
    • Apache Spark Connector for SQL Server and Azure SQL - performance connector that enables you to use transactional data in big data analytics and persists results for ad-hoc queries or reporting. The connector allows you to use any SQL database, on-premises or in the cloud, as an input data source or output data sink for Spark jobs.
    • OpenCV - time computer vision applications. The C++, Python, and Java interfaces support Linux, MacOS, Windows, iOS, and Android.
    • Azure Databricks - based big data analytics service designed for data science and data engineering. Azure Databricks, sets up your Apache Spark environment in minutes, autoscale, and collaborate on shared projects in an interactive workspace. Azure Databricks supports Python, Scala, R, Java, and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch, and scikit-learn.
    • AutoGluon - accuracy deep learning models on tabular, image, and text data.
    • TensorFlow - to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.

  • Python Frameworks, Libraries, and Tools

  • Python Learning Resources

  • R Learning Resources

  • R Tools, Libraries, and Frameworks

    • Rmarkdown
    • Plotly
    • Metaflow - 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.
    • LightGBM
    • MLR
    • Plumber
    • Drake - focused pipeline toolkit for reproducibility and high-performance computing.
    • DiagrammeR
    • Knitr - purpose literate programming engine in R, with lightweight API's designed to give users full control of the output without heavy coding work.
    • Broom
    • Rplugin
    • ML workspace - in-one web-based IDE specialized for machine learning and data science. It is simple to deploy and gets you started within minutes to productively built ML solutions on your own machines. ML workspace is the ultimate tool for developers preloaded with a variety of popular data science libraries (Tensorflow, PyTorch, Keras, and MXnet) and dev tools (Jupyter, VS Code, and Tensorboard) perfectly configured, optimized, and integrated.
    • Shiny
Programming Languages
Python 13 Julia 10 C++ 10 Java 4 MATLAB 3 Kotlin 3 Scala 2 C# 2 HTML 2 Nim 1
Categories
MATLAB Tools, Libraries, Frameworks 32 C/C++ Tools and Frameworks 32 C/C++ Learning Resources 29 ML Frameworks, Libraries, and Tools 27 Java Tools and Frameworks 26 Python Frameworks, Libraries, and Tools 25 Julia Tools, Libraries and Frameworks 21 CUDA Tools Libraries, and Frameworks 20 Learning Resources for ML 16 MATLAB Learning Resources 14 R Tools, Libraries, and Frameworks 13 Python Learning Resources 12 R Learning Resources 12 Java Learning Resources 11 Julia Learning Resources 10 CUDA Learning Resources 6 License 1
Sub Categories
Keywords
python 14 cuda 9 deep-learning 8 cpp 8 java 7 machine-learning 6 gpu 6 neural-network 4 pytorch 4 nvidia 4 data-science 4 julia 4 cpp11 3 cpp14 3 cxx14 3 c 3 matlab 3 tensorflow 3 neural-networks 3 android 3 iot 2 cxx17 2 tensor 2 cli 2 cxx11 2 cxx 2 cpp20 2 cloud 2 cplusplus 2 cpp17 2 numpy 2 rest 2 algorithms 2 machine-learning-algorithms 2 statistics 2 kotlin 2 cxx20 2 gpu-computing 2 azure-sdk 2 azure 2 compiler 2 nvidia-hpc-sdk 2 developer-tools 2 performance 2 visual-studio 2 data-visualization 2 nlp 2 http 2 docker 2 anaconda 1