Neural Networks

Understanding the building blocks of modern artificial intelligence

What are Neural Networks?

Neural networks are computational models inspired by the human brain. They consist of layers of interconnected nodes (neurons) that process and transmit information, enabling the network to learn patterns and make predictions from data.

Basic Neural Network Architecture

A typical feedforward neural network with input layer, hidden layers, and output layer

Neurons & Activation

Learn about artificial neurons, activation functions, and how they process information to create complex patterns.

Training & Optimization

Explore backpropagation, gradient descent, and various optimization techniques used to train neural networks.

Loss Function Visualization

Interactive visualization showing how neural networks optimize their parameters during training.

Implementation Example

import torch
import torch.nn as nn

class SimpleNN(nn.Module):
    def __init__(self):
        super(SimpleNN, self).__init__()
        self.layer1 = nn.Linear(784, 128)
        self.layer2 = nn.Linear(128, 64)
        self.layer3 = nn.Linear(64, 10)
        self.relu = nn.ReLU()
        
    def forward(self, x):
        x = self.relu(self.layer1(x))
        x = self.relu(self.layer2(x))
        x = self.layer3(x)
        return x

# Create model instance
model = SimpleNN()