What is Deep Learning?

Key Concepts:

Popular Architectures:

Explain

1: Key Concepts: Neural Networks: encompass interconnected nodes (neurons) prepared in layers—enter, hidden, and output layers.

Activation functions: features like ReLU and Sigmoid introduce non-linearity into the network, permitting it to analyze complex patterns.

Backpropagation: The set of rules used for schooling neural networks via minimizing the mistake via gradient descent.

2: Popular Architectures: Convolutional Neural Networks (CNNS): great for photograph and video popularitys task. They use convolutional layers to extract functions.

Recurrent Neural Networks (RNNs): suitable for sequential data like time series or textual content. They keep a form of memory thru loops.

Transformers: relatively effective for herbal language processing obligations. They use self-interest mechanisms to weigh the importance of various words in a sentence.