{"id":35937,"date":"2024-11-01T09:44:04","date_gmt":"2024-11-01T09:44:04","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=35937"},"modified":"2024-11-01T11:09:48","modified_gmt":"2024-11-01T11:09:48","slug":"machine-learning-and-deep-learning-algorithm-trading-how-to-build-gans-using-tensorflow-2","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/35937\/","title":{"rendered":"Machine Learning and Deep Learning Algorithm Trading, How to Build GANs Using TensorFlow 2"},"content":{"rendered":"

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1. Introduction<\/h2>\n

In recent years, advances in artificial intelligence and machine learning technologies have made significant progress, and their importance is growing in the financial market, especially in the field of algorithmic trading. In this course, we will cover how to use a model of deep learning known as Generative Adversarial Network (GAN) to generate data and establish trading strategies based on that data. We will particularly provide a step-by-step guide on how to build a GAN using TensorFlow 2.<\/p>\n

2. Basic Concepts of GAN<\/h2>\n

A GAN consists of two neural networks, namely the Generator and the Discriminator. The Generator creates fake data that looks like real data, while the Discriminator determines whether the data is real or generated by the Generator. These two neural networks compete against each other in learning, allowing the Generator to produce data that is more similar to the real data.<\/p>\n

2.1. Structure of GAN<\/h3>\n

The basic structure of a GAN is as follows:<\/p>\n