An Interactive Neural Network Visualization Tool
Neural networks form the foundation of modern artificial intelligence and deep learning systems. Understanding how these computational models process information, learn from data, and make predictions is essential for anyone studying machine learning, computer science, or data science.
This interactive visualization platform provides a step-by-step exploration of neural network fundamentals. Through hands-on experimentation with weights, biases, and activation functions, you'll develop an intuitive understanding of forward propagation, loss calculation, and backpropagation—concepts often obscured by mathematical notation alone.
Every mathematical operation is displayed visually in real-time. See weighted sums, activation functions, and gradients as they flow through the network.
Adjust weights, biases, and learning rates directly. Observe how changes propagate through the network and affect outputs and loss values.
Begin with single neurons and simple datasets. Advance to multi-layer networks, backpropagation, and training procedures at your own pace.
A structured sequence of interactive lessons, each building upon previous concepts.
Learn how a single neuron processes inputs with weights to produce an output
Learning Objectives:
Learn why neural networks need multiple layers by solving the classic XOR problem
Learning Objectives: