Machine Learning

Temporal Difference (TD) Learning is a fundamental concept in the field of reinforcement learning, which is a subfield of artificial intelligence (AI). It is particularly powerful for problems where an agent must learn to make decisions over time based on its interactions with an environment. Unlike traditional supervised learning, where a model learns from a fixed dataset, TD Learning enables agents to learn directly from experience, making it well-suited for dynamic and uncertain environments.

A neural network is like a highly sophisticated, multi-layered calculator that learns from data. It consists of numerous “neurons” (tiny calculators) connected in layers, with each layer performing a unique function to help the network make predictions or decisions.

The beauty of VAEs lies in their ability to generate new samples by randomly sampling vectors from this known region and then passing them through the generator part of our model.

An autoencoder begins its journey by compressing input data into a lower dimension. It then endeavors to reconstruct the original input from this compressed representation.