Neural Network

A neural network is a computational system loosely inspired by biological brains. It consists of interconnected nodes (artificial neurons) organized in layers that process information and learn patterns from data.

Basic structure:

• Input layer: Receives data (pixels, words, numbers)
• Hidden layers: Process and transform the data
• Output layer: Produces results (predictions, classifications, text)

How neurons work: Each neuron:

1. Receives inputs from connected neurons
2. Multiplies each input by a weight
3. Sums the weighted inputs
4. Applies an activation function
5. Passes the result to the next layer

Training adjusts the weights so the network produces correct outputs.

Feedforward networks Information flows one direction, input to output. Basic architecture for classification and regression.

Convolutional Neural Networks (CNNs) Specialized for images. Use filters that detect features like edges, shapes, and patterns. Power image recognition, object detection.

Recurrent Neural Networks (RNNs) Have connections that loop back, giving them memory. Used for sequences before transformers dominated.

Transformers Use attention mechanisms to process sequences. Foundation of modern LLMs and vision models.

Generative Adversarial Networks (GANs) Two networks compete: one generates, one discriminates. Used for image generation.

Autoencoders Compress data to a smaller representation, then reconstruct. Used for dimensionality reduction, anomaly detection.

Training process:

1. Forward pass: Data flows through the network, producing an output
2. Loss calculation: Compare output to correct answer, measure error
3. Backpropagation: Calculate how each weight contributed to the error
4. Update weights: Adjust weights to reduce error
5. Repeat: Process many examples until accuracy improves

Key concepts:

Loss function: Measures how wrong the network is. Training minimizes this.

Learning rate: How much to adjust weights each step. Too high = unstable; too low = slow learning.

Epochs: Complete passes through the training data.

Batch size: Number of examples processed before updating weights.

Overfitting: When the network memorizes training data but fails on new data. Combat with regularization, dropout, more data.

Parameters: Neural networks are defined by their parameters (weights). Modern scale:

• Small network: thousands of parameters
• Large image model: millions
• Large language model: billions to trillions

GPT-3: 175 billion parameters GPT-4: Estimated >1 trillion parameters Claude 3: Undisclosed, likely hundreds of billions

Why scale matters: Larger networks can learn more complex patterns. Research shows capabilities emerge at certain scales that smaller models don't have.

Compute requirements: Training large models requires thousands of GPUs running for months. Inference (running the model) is much cheaper but still substantial.

Efficiency trends: Better architectures and training techniques extract more capability from fewer parameters. A 2024 model often outperforms a larger 2022 model.

You probably use neural networks daily:

• Voice assistants (speech recognition)
• Photo apps (face detection, image enhancement)
• Search engines (understanding queries)
• Translation apps
• Recommendation systems
• Spam filters
• Navigation apps (traffic prediction)

Building neural networks: Most practitioners use frameworks:

• PyTorch: Flexible, research-friendly
• TensorFlow: Production-focused
• Hugging Face: Pre-trained models
• No-code platforms: Train without coding

Or use pre-trained models: For most applications, using a pre-trained model (GPT-4, Claude, open-source models) is more practical than training from scratch. Training large models requires resources most organizations don't have.