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 process information.
- Layers: Neural networks consist of multiple layers, including input, hidden, and output layers.
- Activation Functions: These functions help to determine whether a neuron should be activated or not.
- Backpropagation: This is a process where the network adjusts its weights and biases based on the error of its predictions.
Applications
Deep learning has been successfully applied in various fields, such as:
- Image Recognition: Identifying objects, animals, and faces in images.
- Speech Recognition: Converting spoken words into text.
- Natural Language Processing (NLP): Understanding and generating human language.
- Medical Diagnosis: Analyzing medical images and predicting diseases.
Resources
For more information on deep learning, check out our comprehensive guide on Deep Learning Fundamentals.
Image Example
Here's an example of a neural network: