Neural Network Forward Pass
Join us on a journey to predict whether a student named Alex will pass tomorrow's test! Using Alex's study time, sleep, and previous score, we'll watch a neural network make its prediction step-by-step. You'll see how data flows through layers of neurons, how patterns are detected, and how the network arrives at its final decision. This is the same fundamental process used by all neural networks - from image recognition to language models!
What You'll Learn
Prerequisites
Recommended knowledge before starting this visualization
- Basic understanding of functions
- No advanced math required!
Interactive Visualization
š The Problem: Will This Student Pass?
Meet Alex, a student preparing for tomorrow's exam. We have three pieces of information about Alex:
ā¢ Hours Studied Today: 7 hours
ā¢ Hours Slept Last Night: 8 hours
ā¢ Previous Test Score: 85%
Can we predict if Alex will pass (ā„70%) or fail (<70%) tomorrow's test? Let's use a neural network to find out!
This network has been "trained" on data from thousands of students to recognize patterns between study habits and test outcomes.
Neural Network State
Real-time calculation with actual math
Key Takeaways
š§® Weighted Sums
Each neuron computes a weighted sum of its inputs plus a bias. This linear combination is the foundation of how neurons process information.
ā” Activation Functions
Non-linear activation functions allow networks to learn complex patterns. Without them, multiple layers would collapse to a single linear transformation.
š Layer-by-Layer Processing
Information flows sequentially through layers. Each layer transforms the data, building increasingly abstract representations from input to output.
šÆ Predictions
The output layer produces the network's predictions. During training, these predictions are compared to true values, and errors are used to adjust weights.
Next Steps
Continue your learning journey
Now that you understand forward propagation, learn how networks optimize their weights:
Continue to Gradient Descent