🎓How I Study AIHISA
📖Read
📄Papers📰Blogs🎬Courses
💡Learn
🛤️Paths📚Topics💡Concepts🎴Shorts
🎯Practice
🧩Problems🎯Prompts🧠Review
Search

Concepts3

Category

🔷All∑Math⚙️Algo🗂️DS📚Theory

Level

AllBeginnerIntermediateAdvanced
Filtering by:
#function approximation
📚TheoryAdvanced

Reinforcement Learning Theory

Reinforcement Learning (RL) studies how an agent learns to act in an environment to maximize long-term cumulative reward.

#reinforcement learning#mdp#bellman equation+12
📚TheoryAdvanced

Neural Network Expressivity

Neural network expressivity studies what kinds of functions different network architectures can represent and how efficiently they can do so.

#neural network expressivity#depth separation#relu linear regions+12
📚TheoryIntermediate

Universal Approximation Theorem

The Universal Approximation Theorem (UAT) says a feedforward neural network with one hidden layer and a non-polynomial activation (like sigmoid or ReLU) can approximate any continuous function on a compact set as closely as we want.

#universal approximation theorem#cybenko#hornik+12