Multilayer Perceptron, Explained: A Visual Guide with Mini 2D Dataset
CLASSIFICATION ALGORITHM
⛳️ More [Classification ALGORITHM](https://medium.com/@samybaladram/list/classification-algorithms-b3586f0a772c), explained: · [Dummy Classifier](https://towardsdatascience.com/dummy-classifier-explained-a-visual-guide-with-code-examples-for-beginners-009ff95fc86e) · [K Nearest Neighbor Classifier](https://towardsdatascience.com/k-nearest-neighbor-classifier-explained-a-visual-guide-with-code-examples-for-beginners-a3d85cad00e1) · [Bernoulli Naive Bayes](https://towardsdatascience.com/bernoulli-naive-bayes-explained-a-visual-guide-with-code-examples-for-beginners-aec39771ddd6) · [Gaussian Naive Bayes](https://towardsdatascience.com/gaussian-naive-bayes-explained-a-visual-guide-with-code-examples-for-beginners-04949cef383c) · [Decision Tree Classifier](https://towardsdatascience.com/decision-tree-classifier-explained-a-visual-guide-with-code-examples-for-beginners-7c863f06a71e) · [Logistic Regression](https://towardsdatascience.com/logistic-regression-explained-a-visual-guide-with-code-examples-for-beginners-81baf5871505) · [Support Vector Classifier](https://towardsdatascience.com/support-vector-classifier-explained-a-visual-guide-with-mini-2d-dataset-62e831e7b9e9) ▶ [Multilayer Perceptron](https://towardsdatascience.com/multilayer-perceptron-explained-a-visual-guide-with-mini-2d-dataset-0ae8100c5d1c)
Ever feel like neural networks are showing up everywhere? They're in the news, in your phone, even in your social media feed. But let's be honest – most of us have no clue how they actually work. All that fancy math and strange terms like "backpropagation"?
Here's a thought: what if we made things super simple? Let's explore a Multilayer Perceptron (MLP) – the most basic type of neural network – to classify a simple 2D dataset using a small network, working with just a handful of data points.
Through clear visuals and step-by-step explanations, you'll see the math come to life, watching exactly how numbers and equations flow through the network and how learning really happens!
Definition
A Multilayer Perceptron (MLP) is a type of neural network that uses layers of connected nodes to learn patterns. It gets its name from having multiple layers – typically an input layer, one or more middle (hidden) layers, and an output layer.
Each node connects to all nodes in the next layer. When the network learns, it adjusts the strength of these connections based on training examples. For instance, if certain connections lead to correct predictions, they become stronger. If they lead to mistakes, they become weaker.
This way of learning through examples helps the network recognize patterns and make predictions about new situations it hasn't seen before.
