Five Machine Learning Methods Crypto Traders Should Know About

Oct 16, 2020 at 12:45 UTCUpdated Oct 16, 2020 at 15:46 UTC.In a recent article, I discussed the relevance of the machine learning techniques powering the famous OpenAI's GPT-3 could have for the crypto market.

That's because of the the digital DNA and the transparency of crypto assets and that the rise of crypto has coincided with a renaissance of machine learning and the emergence of deep learning.

Below, I've listed five emerging areas of deep learning that are particularly important to crypto quant scenarios.

Labeled datasets are scarce in the crypto space and that severely limits the type of machine learning quant models that can be built in real world scenarios.

Semi-supervised learning is a deep learning technique that focuses on the creation of models that can learn with small labeled datasets and a large volume of unlabeled data.

Feature extraction and selection are a key component of any quant machine learning model and is particularly relevant in problems that are not very well understood such as crypto asset predictions.

Representation learning is an area of dep learning focused on automating the learning of solid representations or features in order to build more effective models.

Neural architecture search is one area of deep learning that tries to automate the creation models using machine learning.

Sort of using machine learning to create machine learning.

The methods described above represent some emerging and more developed areas of deep learning that are likely to have an impact in the crypto quant models in the short term.