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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.

🚀 Real-world DevOps projects for aspiring engineers — Beginner to Advanced. Covers AWS, Kubernetes, Docker, CI/CD, Terraform, Jenkins, and more. Hands-on learning with step-by-step guides.

  • devops
  • devops-learning
  • devops-poc
  • devops-project

Enjoy our curated collection of examples and solutions. Use these patterns to build your own robust and scalable applications.

Complete Practical Study Plan to become a successful cybersecurity engineer based on roles like Pentest, AppSec, Cloud Security, DevSecOps and so on...

  • aws-security
  • study-guide
  • study-plan
  • appsec

This is a code repository for the corresponding video tutorial. Using React, Node.js, Express & MongoDB you'll learn how to build a Full Stack MERN Application - from start to finish. The App is called "Memories" and it is a simple social media app that allows users to post interesting events that happened in their lives.

Source code for pbrt, the renderer described in the third edition of "Physically Based Rendering: From Theory To Implementation", by Matt Pharr, Wenzel Jakob, and Greg Humphreys.