Reinforcement Learning

The financial market has become one of the first to adopt the machine learning (ML) market. Since the 1980s, people have been using ML to discover the laws of the market.

The simplest definition of machine learning comes from what Berkeley said: Machine learning is a branch of AI that explores ways to make computers more efficient based on experience.

Machine learning is the most advanced aspect of the field of artificial intelligence today, and more beginners have begun to enter this field.

There are many articles on machine learning algorithms that detail the related algorithms, it is still very difficult to make the most appropriate choices.

This article is by no means comprehensive, but rather highlights the pitfalls we have seen over and over. For example, we won't talk about how to choose a good project. Some of our recommendations are generally applicable to machine learning, especially for deep learning or reinforcement learning research projects.

The most important algorithm is the neural network, which is not very successful due to overfitting (the model is too powerful, but the data is insufficient). Still, in some more specific tasks, the idea of using data to adapt to functionality has achieved significant success, and this also forms the basis of today's machine learning.