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A comprehensive Domain-Driven Design example with problem space strategic analysis and various tactical patterns.

  • domain-driven-design
  • ddd
  • ddd-architecture
  • c4

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.

Navigate the complex landscape of .NET software architecture with our step-by-step, story-like guide. Unpack the interplay between modular monoliths, microservices, domain-driven design, and various architectural patterns. Go beyond the one-size-fits-all solutions and understand how to blend these approaches based on your unique needs.