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: Deep learning uses neural networks, which are inspired by the human brain's structure and function.
- Layers: A neural network consists 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 technique used to train deep learning models by adjusting the weights and biases based on the error rate.
Common Deep Learning Models
- Convolutional Neural Networks (CNNs): Excellent for image recognition and processing.
- Recurrent Neural Networks (RNNs): Ideal for sequential data, such as time series or natural language.
- Generative Adversarial Networks (GANs): Used for creating new data that is similar to real-world data.
Applications
- Image Recognition: Deep learning models can identify objects, faces, and scenes in images.
- Speech Recognition: These models can convert spoken words into written text.
- Natural Language Processing (NLP): Deep learning is used for understanding and generating human language.
- Autonomous Vehicles: Deep learning models are crucial for enabling self-driving cars to navigate safely.
Neural Network Diagram
For more information on deep learning, you can explore our Deep Learning Tutorial.