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
- Artificial Neural Networks (ANNs): Mimic the human brain to recognize patterns in data.
- Neural Networks: Composed of neurons, each connected to others, with each neuron responsible for processing a small portion of the input data.
- Layers: Neurons are organized into layers (input, hidden, output), each with a specific function in the learning process.
Learning Process
- Input Layer: Receives raw data and passes it on to the next layer.
- Hidden Layers: Process the input data, transforming it through various algorithms.
- Output Layer: Produces the final result based on the processed data.
Applications
- Image Recognition: Identify objects, faces, and other features in images.
- Natural Language Processing (NLP): Translate, analyze, and generate human language.
- Autonomous Vehicles: Navigate and make decisions on the road.
- Medical Diagnostics: Identify diseases and conditions from medical images.
Neural Network
Resources
For further reading, check out our Deep Learning Course. This comprehensive course covers the fundamentals of deep learning and provides hands-on experience with popular frameworks.