Deep Learning: Multi-Layered Perceptron (MLP)

Author(s): Chandra Prakash Bathula

Originally published on Towards AI.

Stacking Neurons: The Foundation of Deep Learning

Now that we’ve understood what a Perceptron is a simplified model of a single neuron and how Logistic Regression is conceptually similar to it (except for the activation function), the next question is: what comes next?

The article explores the concept of Multi-Layered Perceptrons (MLPs) as an evolution of single neuron models (Perceptrons) and dives into how these networks can learn complex, non-linear patterns through interconnected layers of neurons. It discusses the mathematical operations involved, such as weighted sums and activation functions, and highlights the importance of these functions in enabling the network to approximate complex relationships in data. Additionally, it touches on various models and algorithms in machine learning, explaining how MLPs can outperform traditional models in solving intricate problems.

Read the full blog for free on Medium.

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Published via Towards AI