Introduction to Deep Learning
Deep learning is a branch of artificial intelligence that works on the basis of artificial neural networks and learns from large data sets. It provides high performance, especially on complex data.
Table Of Content
Basic Deep Learning Models
Artificial Neural Networks (ANN)
It provides learning through multi-layered processing of data. It consists of input layer, hidden layers and output layer.
Convolutional Neural Networks (CNN)
- Optimized for image processing and recognition.
- It extracts local features and creates spatial hierarchy.
Recurrent Neural Networks (RNN)
- Suitable for time series and natural language processing.
- It performs operations by keeping past information in its memory.
Advanced Techniques
Transfer Learning
It is the use of pre-trained models in other tasks. It reduces training time and can improve performance.
Regularization Methods
- Dropout: Prevents overlearning.
- Batch Normalization: Increases training speed and ensures stability.
Optimization Techniques
- The learning process is improved with algorithms such as Adam and RMSprop.
- The overall performance of the model is increased by learning rate adjustment.
Application Areas
- Image and voice recognition
- Natural language processing
- autonomous vehicles
- medical diagnosis
Conclusion
Deep learning techniques play a critical role in the evolution of artificial intelligence. More successful results are achieved with advanced applications, powerful models and appropriate optimization.
Last Update:
January 31, 2026
