What is deep learning, Meaning And work?

Deep learning is a subfield of machine learning, which is a broader field of artificial intelligence (AI). Deep learning focuses on training artificial neural networks to perform tasks that typically require human-like intelligence. These neural networks are inspired by the structure and function of the human brain, consisting of interconnected nodes or “neurons” organized into layers.

Here’s a breakdown of the key aspects of deep learning:

1. Neural Networks: Deep learning models are typically built using artificial neural networks. These networks consist of layers of interconnected nodes, with an input layer, one or more hidden layers, and an output layer. Each connection between nodes has a weight that is learned during training.
2. Representation Learning: Deep learning excels at automatically learning and extracting meaningful representations or features from raw data. This ability to discover hierarchical features from data is one of the strengths of deep learning.
3. Training with Data: Deep learning models are trained using large datasets. During training, the model adjusts its internal parameters (the weights of connections between nodes) to minimize the difference between its predictions and the actual target values in the training data.
4. Non-linearity: Neural networks introduce non-linearities through activation functions in each neuron. This allows them to capture complex relationships in data.
5. Deep Architectures: “Deep” in deep learning refers to the presence of multiple hidden layers in neural networks. These deep architectures can model intricate patterns and relationships in data.

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