AI concept
A Multilayer Perceptron (MLP) is a class of feedforward artificial neural network consisting of input, hidden, and output layers trained via gradient-descent backpropagation.
Computer Science AI Problem Lab
Multilayer Perceptron Neural Network Interactive Visualizer
Interactive neural network simulator: configure layers, train on non-linear datasets, adjust learning rates, and visualize real-time decision boundary convergence. Learn the model, state transitions, search behavior, and practical AI reasoning flow through an OpenLabs interactive simulator.
AI Visualizer
Forward pass activations, loss gradients, and weight updates
Neural Architecture & Synapses
x1
x2
h1
h2
h3
ŷ
Loss: 0.042 • Acc: 98.4%
Step 1
Input feature vectors
Step 2
Hidden layer activations (ReLU/Sigmoid)
Step 3
Output prediction & loss calculation
Step 4
Backpropagation weight updates
Reasoning flow
Input signals propagate forward through weighted layers and non-linear activations; error losses are computed against targets, and backpropagated gradients update weights.
Model focus
Forward pass activations, loss gradients, and weight updates
Visualization
Trace each state, decision, or rule step through an interactive learning flow.
Learn by simulating
Understand Neural Network with step-by-step AI reasoning
Input signals propagate forward through weighted layers and non-linear activations; error losses are computed against targets, and backpropagated gradients update weights. The lab turns abstract AI problem solving into a visible sequence of states, decisions, and results.
Understand artificial neuron mathematical models (weights, biases, activations).
Trace forward propagation and non-linear activation functions.
Visualize error backpropagation and gradient descent optimization.
Explore non-linear decision boundary classification and convergence.
Where this AI concept is used
- Binary and multi-class classification
- Pattern and digit recognition
- Function approximation and regression
- Non-linear decision boundary mapping
How the interactive lab works
Open the Neural Network lab, adjust the problem inputs, and follow how the visualizer updates each state or inference step. Use it to compare AI theory with observable behavior.
Curriculum Continuity
View All AI ProblemsExplore Sibling AI & Search Experiments
Variables, domains, and constraintsBacktracking search with constraint checking
Constraint Satisfaction Problem
Explore how AI solves constraint satisfaction problems by assigning values to variables while respecting rules and pruning invalid choices.
Rule-based AI inferenceFacts, rules, goals, and inference paths
Forward and Backward Chaining
Visualize rule-based reasoning as AI moves from facts to conclusions or works backward from a goal to required facts.
Search and guessing strategyState space search and probabilistic guessing
Hangman AI Problem
Study how an AI agent can reason about hidden words, update possible candidates, and choose letters based on available evidence.
Local search optimizationHeuristic optimization over neighboring states
Hill Climbing Search
Watch hill climbing move from one state to a better neighboring state until it reaches a peak or gets stuck in a local optimum.
Reinforcement learningStates, actions, rewards, and Q-value updates
Maze Q-Learning
Explore reinforcement learning by watching an agent move through a maze, collect rewards, update Q-values, and learn better paths.
Classical AI planningState-space planning and goal achievement
Monkey Banana Problem
Visualize how an AI planner solves the classic monkey banana problem by sequencing actions to reach a goal state.
State-space searchState transitions and goal search
Water Jug Problem
Solve the classic water jug problem by pouring, filling, and emptying jugs while tracing each state toward the target amount.
Neural Network FAQs
What is a Multilayer Perceptron (MLP)?
An MLP is a deep feedforward artificial neural network composed of multiple layers of nodes, capable of learning non-linear relationships via supervised backpropagation.
How does backpropagation work?
Backpropagation calculates the gradient of the loss function with respect to each weight using the chain rule of calculus, adjusting weights in the opposite direction of the gradient.
Why are activation functions necessary?
Without non-linear activation functions like ReLU or Sigmoid, stacking multiple linear layers would collapse mathematically into a single linear transformation.
Ready to explore Neural Network?
Launch the visualizer and turn AI search, reasoning, and planning into a clear hands-on learning path.