Advanced Concepts in Neural Networks 🧠

Table of Contents

• Attention Mechanisms
• Transformer Architecture
• Generative Adversarial Networks (GANs)
• Reinforcement Learning

Attention Mechanisms

Attention significantly enhances neural networks by allowing them to focus on the relevant parts of input data, providing a mechanism to weigh the significance of different data components for various tasks.

Transformer Architecture

The Transformer model, often used in NLP applications, is pivotal for understanding complex sequences. It employs self-attention to capture dependencies in the data.

Generative Adversarial Networks (GANs)

GANs consist of two neural networks, a generator and a discriminator, which compete against each other to create data that mimics real-world examples.

Reinforcement Learning

Reinforcement Learning enables models to learn by interacting with the environment, using rewards and punishment to shape behavior.