Visualizing Neural Networks

Understanding how neural networks work can be challenging without proper visualization. In this interactive guide, we'll explore the architecture and training dynamics of neural networks using 3D visualizations and animated charts.

Network Architecture

A neural network consists of layers of interconnected nodes (neurons). Each connection has a weight that determines its importance in the computation.

The network below shows a simple architecture with 4 input nodes, two hidden layers with 6 nodes each, and 2 output nodes.

Training Dynamics

During training, the network adjusts its weights to minimize the error between predicted and actual outputs. This process is called backpropagation.

Loss Over Time

The loss function measures how well the network is performing. A decreasing loss indicates the network is learning.

Key Concepts

Forward Propagation

1. Input data enters through the input layer
2. Each neuron computes a weighted sum of its inputs
3. An activation function (like ReLU) is applied
4. The result passes to the next layer

Backpropagation

The network learns by:

1. Computing the error at the output
2. Propagating gradients backward through layers
3. Updating weights to reduce error

Hyperparameters

Key hyperparameters that affect training:

Code Example

Here's a simple neural network implementation in PyTorch:

import torch
import torch.nn as nn
 
class NeuralNetwork(nn.Module):
    def __init__(self, input_dim, hidden_dims, output_dim):
        super().__init__()
 
        layers = []
        prev_dim = input_dim
 
        for hidden_dim in hidden_dims:
            layers.extend([
                nn.Linear(prev_dim, hidden_dim),
                nn.ReLU(),
                nn.Dropout(0.1)
            ])
            prev_dim = hidden_dim
 
        layers.append(nn.Linear(prev_dim, output_dim))
        self.network = nn.Sequential(*layers)
 
    def forward(self, x):
        return self.network(x)
 
# Create a network with architecture [4, 6, 6, 2]
model = NeuralNetwork(
    input_dim=4,
    hidden_dims=[6, 6],
    output_dim=2
)

Conclusion

Visualizing neural networks helps build intuition about how deep learning works. By seeing the flow of information and the dynamics of training, we can better understand and debug our models.

References

• Deep Learning Book by Goodfellow, Bengio, and Courville
• 3Blue1Brown Neural Networks Series
• PyTorch Documentation