#MachineLearning – Page 7 – Interview Patch

Machine Learning and AI Python (with libraries like TensorFlow, Keras, scikit-learn)

What is the purpose of an activation function?

An activation function is a mathematical function used in artificial neural networks to determine the output of a neuron from its inputs. It is used to introduce non-linearity into the network, allowing it to learn more complex functions.

For example, the most commonly used activation function is the Rectified Linear Unit (ReLU). This function takes the input and returns 0 if it is negative, and returns the input if it is positive. This allows the network to learn more complex functions, like distinguishing between different classes of data.

Machine Learning and AI Python (with libraries like TensorFlow, Keras, scikit-learn)

What is the difference between supervised and unsupervised learning?

Vaibhav Kothia May 26, 2023 0 Comments

Supervised learning is a type of machine learning algorithm that uses a known dataset (labeled data) to predict outcomes. It is based on the idea of using input data to predict a certain output. For example, a supervised learning algorithm could be used to predict whether a customer will buy a product based on their past purchasing behavior.

Unsupervised learning is a type of machine learning algorithm that does not require labeled data. Instead, it uses an unlabeled dataset to discover patterns and insights. For example, an unsupervised learning algorithm could be used to cluster customers based on their buying behavior.

What is the purpose of an activation function?

What is the difference between supervised and unsupervised learning?

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