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
Neural Networks: Inspired by the human brain, these networks consist of layers of interconnected nodes, or "neurons," that process and transmit information.
Activation Functions: These functions help determine whether a neuron should be activated or not, based on the input it receives.
Backpropagation: This is the process of adjusting the weights of the neurons in a neural network to minimize the error in the output.
Types of Deep Learning Models
Convolutional Neural Networks (CNNs): Ideal for image recognition and processing tasks.
Recurrent Neural Networks (RNNs): Excellent for sequence data like time series or natural language.
Generative Adversarial Networks (GANs): Used for generating new data with similar statistics to real-world data.
Learning Resources
For more in-depth information on Deep Learning, we recommend checking out our comprehensive guide on Deep Learning Principles.
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