Deep learning is a subset of machine learning that structures algorithms in layers to create an "artificial neural network" that can learn and make intelligent decisions on its own.
Key Concepts
- Neural Networks: Inspired by the human brain, neural networks consist of layers of interconnected nodes (or "neurons") that process information.
- Layers: Deep learning models have multiple layers, including input, hidden, and output layers.
- Activation Functions: These functions help determine whether a neuron should be activated or not based on the input it receives.
- Backpropagation: This is a technique used to adjust the weights of the neurons in a neural network, improving its accuracy over time.
Applications
- Image Recognition: Deep learning models have revolutionized image recognition, enabling applications like facial recognition and object detection.
- Natural Language Processing (NLP): NLP uses deep learning to understand and generate human language, powering tools like chatbots and translation services.
- Medical Diagnosis: Deep learning models can analyze medical images and assist in diagnosing diseases like cancer and diabetes.
Learning Resources
For further reading on deep learning, check out our comprehensive guide on Deep Learning Basics.
Deep Learning Architecture