Deep learning is a subset of machine learning that has gained significant attention in recent years. It involves training neural networks with multiple layers to learn and extract patterns from large amounts of data. In this section, we'll explore some key principles of deep learning.
Key Principles
Data is King: Deep learning models require large amounts of labeled data to train effectively. The quality and quantity of data can significantly impact the performance of the model.
Overfitting and Underfitting: Overfitting occurs when a model learns too much from the training data, including the noise, and performs poorly on new data. Underfitting happens when a model is too simple and cannot capture the underlying patterns in the data. The goal is to find the right balance between the two.
Regularization: Techniques like L1 and L2 regularization are used to prevent overfitting by adding a penalty term to the loss function.
Dropout: Dropout is a regularization technique where randomly selected neurons are ignored during training, which helps prevent overfitting.
Activation Functions: Activation functions add non-linear properties to the neural network, allowing it to learn complex patterns.
Resources
For further reading on deep learning principles, you can check out our Deep Learning Course.