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: Deep learning models have multiple layers, including input, hidden, and output layers.
- Backpropagation: This algorithm adjusts the weights of the neurons to improve the accuracy of the model.
Getting Started
To dive deeper into deep learning, we recommend checking out our Deep Learning Tutorial.
Applications
Deep learning has numerous applications across various fields, including:
- Image Recognition: Identifying objects and features in images.
- Natural Language Processing: Understanding and generating human language.
- Recommender Systems: Personalizing recommendations for users.
Deep Learning Architecture
Resources
For further reading, explore the following resources:
- Deep Learning Book
- TensorFlow: An open-source library for machine learning.
- Keras: A high-level neural networks API, written in Python.