Neural networks are computational models inspired by the human brain, designed to recognize patterns and make decisions. They form the core of modern Artificial Intelligence (AI) and are widely used in tasks like image recognition, natural language processing, and predictive analytics.
🧩 Basic Structure
A neural network consists of layers of interconnected nodes (neurons):
- Input Layer: Receives data (e.g., pixel values in images)
- Hidden Layer(s): Processes data through weighted connections
- Output Layer: Produces the final result (e.g., classification labels)
Each neuron applies a non-linear activation function to its inputs, enabling complex pattern recognition. 🔄
📊 Key Concepts
- Weights & Biases: Parameters adjusted during training to minimize errors
- Backpropagation: Algorithm for efficiently updating weights
- Loss Function: Measures prediction accuracy (e.g., mean squared error)
- Optimization: Techniques like gradient descent to refine model performance
🌍 Applications
Neural networks power numerous technologies:
- Computer Vision (e.g., object detection)computer vision structure
- Natural Language Processing (e.g., chatbots)natural language processing
- Reinforcement Learning (e.g., game-playing AI)reinforcement learning
- Autonomous Systems (e.g., self-driving cars)autonomous systems
📚 Expand Your Knowledge
For deeper insights into AI fundamentals:
🔗 Machine Learning 101
deep learning foundations