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 are composed of interconnected nodes (neurons) that work together to process information.
- Layers: A neural network consists of multiple layers, including input, hidden, and output layers. Each layer performs a specific function in the learning process.
- Activation Functions: These functions help determine whether a neuron should be activated or not, based on the input it receives.
Applications
Deep learning has revolutionized various fields, including:
- Image Recognition: Identifying objects, faces, and activities in images.
- Speech Recognition: Transcribing spoken words into written text.
- Natural Language Processing: Understanding and generating human language.
- Medical Diagnosis: Analyzing medical images and predicting diseases.
Resources
For further reading on deep learning, check out our comprehensive guide on Machine Learning Basics.
Deep Learning Architecture