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Translated:fire: This repository mainly contains introductory Spring Boot learning tutorials and some commonly used Spring Boot practical project tutorials, including various example code for using Spring Boot, as well as the source code and demos of some practical projects. The practical projects include basic web development and the online blog projects / large enterprise mall systems / front-end and back-end separation practice projects that are widely used today, freeing you from the constraints of all kinds of hello world introductory examples so you can truly master Spring Boot development.

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.

Auto-optimizing a neural net (and its architecture) on the CIFAR-100 dataset. Could be easily transferred to another dataset or another classification task.

  • hyperopt
  • hyperparameter-optimization
  • hyperparameter-tuning
  • hyperparameters-optimization