Neural Network

Neural networks process data through layers of interconnected nodes (neurons) wherein neuron applies a non-linear transformation to the input, enabling complex pattern recognition.

How It Works

Common types include Feedforward Networks, Recurrent Neural Networks (RNNs), and Convolutional Neural Networks (CNNs).

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Types of Neural Networks

Training involves feeding the network with labeled data. The network adjusts its weights to minimize the error between predicted and actual outputs.

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Training Neural Networks

Neural networks are used in image recognition, natural language processing, speech recognition, and predictive analytics.

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Applications of Neural Networks

Challenges include the need for large datasets, computational intensity, and the risk of overfitting.

Challenges in Neural Networks

Challenges include the need for large datasets, computational intensity, and the risk of overfitting.

Future of Neural Networks