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): Inspired by the human brain, ANNs are composed of layers of interconnected nodes or "neurons" that process information.
Neural Layers: There are typically three types of layers in a neural network: input, hidden, and output layers.
Activation Functions: These functions determine whether a neuron should be activated or not based on the input it receives.
Types of Deep Learning Models
Convolutional Neural Networks (CNNs): Excellent for image recognition and processing.
Recurrent Neural Networks (RNNs): Ideal for sequential data like time series or natural language.
Generative Adversarial Networks (GANs): Used for generating new data with similar characteristics to real-world data.
Learning Resources
For more in-depth information, check out our comprehensive guide on Deep Learning.