Artificial Neural Networks (ANNs) are computing systems vaguely inspired by the biological neural networks that constitute animal brains. They are the fundamental building blocks of Deep Learning.
How does an Artificial Neuron work?
A neuron takes multiple inputs, multiplies them by weights (importance), adds a bias (threshold), and passes the result through an activation function (non-linearity). If the signal is strong enough, the neuron "fires" and passes information to the next layer.
What are the main types of Neural Networks?
- Feedforward NN: The simplest type. Information moves in one direction.
- CNN (Convolutional Neural Network): Specialized for processing grid-like data (images). It scans the image with filters to detect patterns.
- RNN (Recurrent Neural Network): Designed for sequential data (time series, text). It has a "memory" of previous inputs.
- Transformer: The modern architecture for language. It uses "attention" mechanisms to weigh the importance of different parts of the input data simultaneously.
How do Neural Networks learn?
They learn through a process called Backpropagation. The network makes a guess, compares it to the actual answer to calculate the loss (error), and then works backward to adjust the weights to minimize that error. This is repeated millions of times.
Inside a Neuron
Visualize how a single neuron processes inputs to produce an output.
Quest: The Spark
Adjust the weights and bias to make the neuron fire (Output > 0.8). Think about how the bias shifts the activation threshold.
Signal Flow
Connect the nodes to complete the neural pathway.
Quest: The Network
Connect the neurons to route the signal from the Input Layer (Blue) to the Output Layer (Pink). Click the lines to toggle connections.
Frequently Asked Questions
What is the 'Black Box' problem?
Neural networks can be so complex that even their creators don't fully understand how they arrive at a specific decision. This lack of interpretability is a major challenge in high-stakes fields like medicine.
What is an activation function?
It's a mathematical function (like ReLU or Sigmoid) attached to each neuron that decides whether it should be activated. It introduces non-linearity, allowing the network to learn complex patterns.
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