Activation Function, Hidden Layer and non linearity. _ day 12

Understanding Non-Linearity in Neural Networks Understanding Non-Linearity in Neural Networks Non-linearity in neural networks is essential for solving complex tasks where the data is not linearly separable. This blog post explains why hidden layers and non-linear activation functions are necessary, using the XOR problem as an example. What is Non-Linearity? Non-linearity in neural networks allows the model to learn and represent more complex patterns. In the context of decision boundaries, a non-linear decision boundary can bend and curve, enabling the separation of classes that are not linearly separable. Role of Activation Functions The primary role of an activation function is...

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Regression vs Classification Multi Layer Perceptrons (MLPs) _ day 10

Regression with Multi-Layer Perceptrons (MLPs) Introduction Neural networks, particularly Multi-Layer Perceptrons (MLPs), are essential tools in machine learning for solving both regression and classification problems. This guide will provide a detailed explanation of MLPs, covering their structure, activation functions, and implementation using Scikit-Learn. Regression vs. Classification: Key Differences Regression Objective: Predict continuous values. Output: Single or multiple continuous values. Example: Predicting house prices, stock prices, or temperature. Classification Objective: Predict discrete class labels. Output: Class probabilities or specific class labels. Example: Classifying emails as spam or not spam, recognizing handwritten digits, or identifying types of animals in images. Regression with...

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