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. In this tutorial, we will introduce the basic concepts of deep learning and its applications.
Basic Concepts
- Neural Networks: Deep learning is based on the concept of neural networks, which are inspired by the human brain's neural structure.
- Layers: A neural network consists of multiple layers, including input, hidden, and output layers.
- Activation Functions: Activation functions determine whether a neuron should be activated or not.
- Backpropagation: Backpropagation is a method used to train neural networks by adjusting the weights and biases based on the error rate.
Applications
Deep learning has been applied in various fields, such as:
- Image Recognition: Deep learning models can be used to classify images, such as identifying objects in photographs.
- Natural Language Processing: Deep learning has been used to develop models that can understand and generate human language.
- Recommender Systems: Deep learning can be used to build recommender systems that suggest products or content to users based on their preferences.
Further Reading
For more information about deep learning, you can visit the following resources:
Deep Learning Neural Network