Deep learning

Deep learning involves training algorithms called neural networks on large amounts of data. These networks have multiple layers of interconnected nodes or "neurons" that process and learn patterns from the data.

Data Input: It all starts with a massive amount of data. The more data, the better the model can learn.

Neural Network Architecture: This consists of an input layer, multiple hidden layers, and an output layer. Each neuron in a layer is connected to neurons in the subsequent layer.

Training Process: The network is trained using labeled data. It adjusts the weights of the connections between neurons to minimize the error in predictions.

Backpropagation: This technique is used to fine-tune the weights by propagating the error backward through the network.

Applications

Deep learning powers many modern technologies, such as:

Image and speech recognition: Think facial recognition and voice assistants.

Natural language processing: Used in chatbots and translation services.

Autonomous vehicles: Helps in object detection and decision-making for self-driving cars.

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