Preeti
Neural networks are computational models inspired by the human brain’s neural structure, processing information through interconnected nodes to make predictions or decisions. They consist of input, hidden, and output layers, with weights adjusting connections between neurons and biases allowing for non-zero outputs. Activation functions introduce non-linearity, enhancing the network’s ability to learn complex relationships. Different types of neural networks include Feedforward Neural Networks (FNNs) for basic tasks, Convolutional Neural Networks (CNNs) for image processing, Recurrent Neural Networks (RNNs) for sequential data, and Long Short-Term Memory Networks (LSTMs) for long-term dependencies.
Understanding neural network architecture, types, and use cases is crucial for effectively applying them in various domains. A simple implementation of a feedforward neural network using TensorFlow/Keras is provided, showcasing how to define the model architecture, compile, train, and evaluate it. Neural networks offer versatile applications in machine learning, and selecting the appropriate type based on the task at hand is key to solving real-world problems effectively. Thank you for reading and being a part of the In Plain English community!
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