FAQ

Training EASY Quantum AI: Supervised, Unsupervised, and Reinforcement Learning

The capacity of EASY Quantum AI to predict financial market trends lies in its strategic learning methodology, which includes supervised, unsupervised, and reinforcement learning. Each learning method serves a unique purpose, and together, they equip EASY Quantum AI with the necessary tools to make robust and accurate predictions. Let’s unravel these techniques and interpret how they contribute to the training of EASY Quantum AI.

Supervised Learning: Guiding the Quantum Path

Supervised learning, as the name suggests, is a method that involves teaching the AI model using labeled data — data where the outcome (or label) is already known. The model learns by mapping input variables (features of market data) to the output variable (market trend prediction) using this data set.

In the context of EASY Quantum AI, supervised learning comes into play during the initial stages of the model’s training. Historical market data, including past trends, price movements, and trading volumes, come with known outcomes and are used to train the model.

Unsupervised Learning: Diving into Deep Market Insights

Unlike supervised learning, unsupervised learning involves training the AI model with unlabeled data, meaning the outcomes aren’t known beforehand. Instead, the model is tasked with recognizing patterns, correlations, or clusters in the input data.

Unsupervised learning in EASY Quantum AI allows the model to identify hidden patterns in market behavior and trends that may not be evident from labeled data. It’s this facet of unsupervised learning that makes it an invaluable ally in deciphering the often mysterious movements of the financial markets.