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A curated list of fuzzing resources ( Books, courses - free and paid, videos, tools, tutorials and vulnerable applications to practice on ) for learning Fuzzing and initial phases of Exploit Development like root cause analysis.

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.

Promise packages, patterns, chat, and tutorials

  • async-functions
  • promise-modules
  • promises
  • javascript