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:notebook_with_decorative_cover: :books: A curated list of awesome resources : books, videos, articles about using Next.js (A minimalistic framework for universal server-rendered React applications)

A simplified Jira clone built with React/Babel (Client), and Node/TypeScript (API). Auto formatted with Prettier, tested with Cypress.

A reference .NET application implementing an eCommerce site

Implements a deep pretty printer for Go data structures to aid in debugging

:dash: Writing Fast Ruby :heart_eyes: -- Collect Common Ruby idioms.

  • benchmark-ips
  • performance-optimization
  • ruby
  • hacktoberfest

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.