Deep learning is a subset of machine learning that structures algorithms in layers to create an "artificial neural network" that can learn from large amounts of data.
What is Deep Learning?
Deep learning is a subset of machine learning that structures algorithms in layers to create an "artificial neural network" that can learn from large amounts of data. The neural network can use this learning to make intelligent decisions and predictions based on the data it was trained on.
Key Components of Deep Learning
- Neural Networks: Deep learning uses neural networks, which are inspired by the human brain's structure and function.
- Layers: A neural network consists of layers, including input, hidden, and output layers.
- Weights and Biases: Each layer has weights and biases that help the network learn from the data.
- Backpropagation: This is the process of adjusting the weights and biases based on the error rate of the predictions.
Deep Learning Applications
Deep learning has been applied to various fields, including:
- Image Recognition: Deep learning models can be used to recognize and classify images.
- Speech Recognition: Deep learning can be used to convert spoken words into text.
- Natural Language Processing: Deep learning models can understand and generate human language.
- Medical Diagnosis: Deep learning can help in diagnosing diseases by analyzing medical images.
Further Reading
For more information on deep learning, you can explore our Machine Learning Tutorial.
Image of Neural Network: