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CLI tool and python library that converts the output of popular command-line tools, file-types, and common strings to JSON, YAML, or Dictionaries. This allows piping of output to tools like jq and simplifying automation scripts.

A fast, convenient and nonintrusive conversion framework between JSON and model. Your model class doesn't need to extend any base class. You don't need to modify any model file.

  • objective-c
  • model
  • json
  • dictionary

A lightweight library for converting complex objects to and from simple Python datatypes.

  • serialization
  • deserialization
  • validation
  • python

TranslatedFlyingMouse Format (飞鼠格式) - a free file format conversion tool for Windows (works offline, with FFmpeg/LibreOffice/Poppler/Tesseract built in). Converts between image/document/spreadsheet/PPT/PDF/audio-video/WPS formats + OCR + batch conversion; audio supports only common formats. Author: 牢蜂 (LaoFeng, "jailed bee") | For personal free use only; commercial sale, resale, and re-skinning are prohibited

An NLP library for building bots, with entity extraction, sentiment analysis, automatic language identify, and so more

A suite of utilities for converting to and working with CSV, the king of tabular file formats.

CssToInlineStyles is a class that enables you to convert HTML-pages/files into HTML-pages/files with inline styles. This is very usefull when you're sending emails.

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.

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.